# Patient-Centric Knowledge Graphs: A Survey of Current Methods, Challenges,
and Applications

Hassan S. Al Khatib, Subash Neupane, Harish Kumar Manchukonda, Noorbakhsh
Amiri Golilarz,
Sudip Mittal, Amin Amirlatifi, Shahram Rahimi Dept. of Computer Science &
Engineering, Mississippi State University, Starkville, MS
Email: {hsa78<EMAIL_ADDRESS>{amin}@che.msstate.edu,{amiri,mittal,
<EMAIL_ADDRESS>

###### Abstract

Patient-Centric Knowledge Graphs (PCKGs) represent an important shift in
healthcare that focuses on individualized patient care by mapping the
patient’s health information in a holistic and multi-dimensional way. PCKGs
integrate various types of health data to provide healthcare professionals
with a comprehensive understanding of a patient’s health, enabling more
personalized and effective care. This literature review explores the
methodologies, challenges, and opportunities associated with PCKGs, focusing
on their role in integrating disparate healthcare data and enhancing patient
care through a unified health perspective. In addition, this review also
discusses the complexities of PCKG development, including ontology design,
data integration techniques, knowledge extraction, and structured
representation of knowledge. It highlights advanced techniques such as
reasoning, semantic search, and inference mechanisms essential in constructing
and evaluating PCKGs for actionable healthcare insights. We further explore
the practical applications of PCKGs in personalized medicine, emphasizing
their significance in improving disease prediction and formulating effective
treatment plans. Overall, this review provides a foundational perspective on
the current state-of-the-art and best practices of PCKGs, guiding future
research and applications in this dynamic field.

###### Index Terms:

Knowledge Graph, Patient-Centric, Personalized Healthcare, Natural Language
Processing, Generative AI

##  I Introduction

The healthcare industry has experienced a significant shift, transitioning
from traditional, provider-centric models towards patient-centered care. This
shift highlights the critical role of engaging patients as active participants
in their healthcare journeys. At the center of this transformation are PCKGs,
which significantly advance personalized, data-driven care. PCKGs facilitate
the integration of diverse data types, including medical history, genetics,
lifestyle choices, and real-time data from health technology devices,
fostering a comprehensive view of patient health essential for customizing
treatments to individual needs[1][2].

The evolution of Knowledge Graphs (KG) in healthcare into sophisticated
networks reflects an increasing acknowledgment of the complexity of human
health and the insufficiency of siloed data systems in addressing multifaceted
health issues. This evolution is propelled by the necessity for a holistic
understanding of patient health, enabling personalized care and applying
advanced data analytics to enhance healthcare outcomes[3][4]. PCKGs stand at
the forefront of healthcare innovation, signifying a crucial step towards
integrated, patient-focused knowledge networks. This shift from fragmented
data systems to cohesive KGs enables healthcare providers to employ machine
learning and analytical technologies to help improve precision medicine,
diagnostic accuracy, and treatment efficacy. Such a transition represents a
technological leap that aligns with the broader objectives of healthcare
reform aimed at delivering more personalized, efficient, and patient-centered
care[5][6]. However, incorporating KGs into healthcare presents technical,
methodological, and ethical challenges, including data interoperability,
privacy concerns, and the complexities of modeling diverse health outcomes.
These hurdles pose significant barriers to the widespread adoption of PCKGs,
yet the potential of these systems to revolutionize healthcare by offering a
nuanced and comprehensive understanding of patient health is
unquestionable[7][8].

PCKGs’ primary aim is to enhance the quality of patient care, improve
treatment outcomes, and increase the efficiency of healthcare delivery. By
integrating disparate data sources and utilizing advanced analytical models,
PCKGs promise to deliver personalized, efficient, and effective healthcare
services tailored to each patient’s unique needs. This goal emphasizes the
shift towards a healthcare system that values and prioritizes individual
patient experiences and needs, marking the beginning of a new era of patient-
centric, data-driven care[9][10]. Despite the inherent challenges in
integrating KGs into healthcare, the critical need for the advanced
application of PCKGs to achieve personalized care and enhance healthcare
delivery systems is undeniable. As the healthcare landscape continues to
evolve, PCKGs exemplify the industry’s commitment to leveraging technology to
meet patients’ complex and varied needs, thus marking a significant milestone
in the journey toward more personalized and effective healthcare solutions.

The motivation of this paper stems from the growing need to consolidate
disparate healthcare data into a unified, holistic view for improved patient
care. The key contributions of this survey paper are:

  * •

A foundational explanation of knowledge graphs, serving as the theoretical
basis for the remainder of the paper.

  * •

Presentation of survey findings and introduction of a taxonomy developed for
the field of PCKGs.

  * •

An in-depth review of methodologies specifically designed for PCKGs, shedding
light on the most effective techniques currently employed.

  * •

Exploration of real-world applications and use-cases that have successfully
implemented PCKG methodologies, providing evidence of their utility.

The rest of the paper is organized as follows: Section II explains the
principles of knowledge graphs, which provide a foundation for the discussions
that follow. Section III presents the findings from our survey and introduces
the taxonomy we have developed. Moving on to Section IV, we review
methodologies explicitly designed for PCKGs. Section V explores real-world
applications and examples that benefit from these methodologies. Section VI
highlights research challenges and provides targeted recommendations for
future scholars in this field. Finally, in Section VII, we summarize the key-
findings of this paper and outline directions for work in PCKGs.

##  II Background

The development of knowledge representation has a rich history in the realms
of logic and AI. The notion of graphical knowledge representation can be
traced back to 1956 when Richens [11] introduced the concept of semantic nets.
Similarly, symbolic logic knowledge finds its roots in the General Problem
Solver [12] of 1959. Initially, knowledge bases were employed in knowledge-
based systems for reasoning and problem-solving. Notably, MYCIN [13], an
expert system renowned for medical diagnosis, utilized a knowledge base
containing approximately 600 rules. However, it was in 2012 that the concept
of Knowledge Graph (KG) gained immense popularity, thanks to Google’s search
engine and its introduction of the Knowledge Vault framework [14]. This
framework aimed to construct large-scale KGs through knowledge fusion. There
is currently no consensus on the definition of the term, with several authors
proposing different definitions. Table I illustrates some of these definitions
currently available in the literature.

TABLE I: Various definition of KGs in available literature. Source |  Definition
---|---
Paulheim et al. [15] |  A knowledge graph (i) mainly describes real world entities and their interrelations, organized in a graph, (ii) defines possible classes and relations of entities in a schema, (iii) allows for potentially interrelating arbitrary entities with each other and (iv) covers various topical domains.
Ehrlinger et al. [16] |  A knowledge graph acquires and integrates information into an ontology and applies a reasoner to derive new knowledge.
Wang et al. [17] |  A knowledge graph is a multi- relational graph composed of entities and relations which are regarded as nodes and different types of edges, respectively.
Smirnova et al. [18] |  A Knowledge Graph, also known as a Knowledge Base, is a directed graph formed by triples that connect nodes (subjects) to other nodes (objects) through properties (predicates), where these connections represent semantic relationships and the graph’s structure illustrates the subjects, objects, and their interrelations.
Duan et al. [19] |  A knowledge graph organizes items, entities, and users as nodes interconnected by edges, providing rich semantics and comprehensive information in a structure akin to natural language.

Figure 1: An example of knowledge graph, where the triplet ($v_{9}$, $r_{1}$,
$v_{8}$) serves as an illustration of the link between entities $v_{9}$ and
$v_{8}$ through the relation $r_{1}$ and ($v_{8}$, $r_{2}$, $v_{1}$) through
$r_{2}$ for relation between $v_{8}$ and $v_{1}$. Figure 2: Illustration of
Patient’s Clincal Visits Knowledge Graph

A KG assumes the form of a directed graph $(G)$, characterized by vertices and
edges, where, $G=(V,E)$. Vertices $(V)$ indicate an entity and Edge $(E)$
between the two vertices conveys the semantic relationship between two
entities [20]. Within the graph, vertices, also referred to as entities or
nodes, are linked through relationships represented as edges and facts are
depicted using RDF [21] triples such as (subject, predicate, object) or (head,
relation, tail), denoted by $<h,r,t>$ [22]. Figure 1 depicts a simple KG with
vertex $v_{9}$ and $v_{8}$ linked by the relation $r_{1}$, which goes from
$v_{9}$ to $v_{8}$, making a triplet ($v_{9}$, $r_{1}$, $v_{8}$). In this
scenario, $v_{9}$ represents the head and $v_{8}$ represents the tail. In the
context of Patient Centric Knowledge Graphs(PCKGs) nodes typically represent
entities like patients, drugs, diseases, or genes, whereas edges denote
relationships or associations between these entities (see Fig 2).

Over the past few years, especially within the domains of health and
biomedicine, various forms of KGs have been proposed, often stemming from
literature or Electronic Health Record (EHR), however they are typically not
individual-centered. Recent work pertaining to disease-centric knowledge
graphs are available in [23, 24]. These knowledge graphs facilitate
clinicians’ ability to study the relationships between diseases to answer
practical clinical problems. Nevertheless, because relationships between
disease concepts are frequently distributed across numerous datasets, the
concept of integrating multimodal data sources is critical for developing
comprehensive disease knowledge graphs [23, 24]. Their robustness of knowledge
in terms of diseases and symptoms, on the other hand, must be evaluated in
order to assess its accuracy and quality [25].

In hospitals, patient information collected during visits, as well as details
of test results, clinical notes, symptoms, diagnoses, and drugs, is frequently
maintained as an EHR. These records include both unstructured data in the form
of free notes, such as progress notes, admission notes, discharge summaries,
medical histories, procedures notes, etc., and structured data, such as
medical codes, medications, administrative data, vital signs, and laboratory
test report data [26]. Numerous KGs have been introduced in the literature,
utilizing EHRs as their foundation. As an illustration, Finlayson SG et al.
[27] constructed a graph including diseases, drugs, procedures and devices. To
do so, they leveraged 1 million clinical concepts from 20 million clinical
notes. Similarly, another study in [28], formulated a KG covering 156 diseases
and 491 symptoms, based on the insights drawn from a dataset of 273,174
patient visits to the emergency department. Although some efforts have been
made to incorporate patient-specific information into these graphs, they are
not yet patient-centric.

PCKGs can be linked Personal Knowledge Graphs (PKG), an emerging concept in
this field. Although the notion of PKGs is relatively new, there have been
some notable contributions in this area. One of the pioneering papers that
formally introduces the concept of PKG is authored by Balog et al.[29]. They
define PKG “to be a source of structured knowledge about entities and the
relation between them, where the entities and the relations between them are
of personal, rather than general, importance. The graph has a particular
“spiderweb” layout, where every node in the graph is connected to one central
node: the user”. Within the scope of this definition, the PKG only incorporate
knowledge pertinent to the specific user.

The emergence of KGs has brought about a substantial transformation in the
healthcare domain, providing numerous advantages to this industry. Integrating
disparate health data to provide a comprehensive and holistic view of patient
health is a significant application of KGs. In the era of precision medicine,
wherein therapeutic strategies are customized to suit individual patients’
distinctive genetic composition and lifestyle variables, KGs assume a crucial
function. Integrating various data types, such as genetic profiles, medical
history, lifestyle behaviors, and information from health technology devices,
is facilitated to enhance efficiency. Implementing this all-encompassing
methodology enables more accurate diagnoses, treatment strategies, and
potentially enhanced health results [30].

Moreover, KGs are a formidable instrument for deciphering intricate biomedical
data, resulting in amplified research findings [31]. They provide a systematic
depiction of connections among different entities, such as genes, diseases,
drugs, and pathways, which can be utilized to formulate novel hypotheses and
uncover concealed patterns. For example, KGs can elucidate the intricate
relationship between a genetic mutation and its association with a specific
disease or unveil the influence of environmental factors in the progression of
said disease. As mentioned earlier, the discoveries possess the potential to
significantly contribute to the development of groundbreaking therapeutic
approaches and expand the horizons of biomedical investigation [32].

KGs play a crucial role in developing effective decision support systems in
the clinical domain. By effectively amalgamating and analyzing individualized
patient data, KGs can assist medical practitioners in formulating accurate
diagnostic determinations, devising optimal treatment strategies, and
forecasting patient prognoses. Consequently, the implementation of KGs can
effectively mitigate the occurrence of medical errors [33].

KGs have found significant applications across various sectors beyond
healthcare. For example, in e-commerce, KGs are extensively used to enhance
user experience through personalized recommendations, search result ranking,
and semantic search. Prominent e-commerce platforms, such as Walmart, Amazon
and eBay, have been utilizing KGs to link items to their respective
attributes, thereby providing more relevant product suggestions to customers
[34]. Moreover, KGs are increasingly used in Natural Language Processing
(NLP), where they can improve the performance of machine translation,
question-answering, and text summarization systems [35]. KGs also have
substantial potential in cybersecurity, as they can aid in mapping
relationships between cyber threats, vulnerabilities, and affected systems,
facilitating better threat prediction and prevention [36]. Social networking
platforms like Meta and LinkedIn also leverage KGs to understand user
relationships and interests, enhancing content recommendation and advertising
targeting strategies [37]. Hence, the applications of KGs are diverse and
significantly impact various domains, reinforcing their relevance and
potential.

##  III Developed Taxonomy of Patient-Centric Knowledge Graph

Having introduced the concept of KG in the previous section, we now explore
the survey’s core. This section will examine the various approaches,
categorizations, and details that influence PCKGs. PCKGs are characterized by
a variety of methodologies and studies. In order to navigate this diverse
landscape, we need a well-structured framework. In this context, a taxonomy is
a guide that enables readers to comprehend and classify the complexities
involved in PCKG construction, evaluation, processing, and applications.

Our developed taxonomy as depicted in Fig 3 is derived from an exhaustive
survey and analysis of existing literature and practices. Based on our
findings, we classify PCKG into four major category including construction,
evaluation, process and applications. Given the heterogeneity of studies in
the domain, it provides a structured representation and insight into the why
and how of each categorization. The taxonomy presented in this paper outlines
essential relationships and highlights the important aspects of each of these
categories. Its uniqueness lies in its exhaustive nature, informed by the
foundational literature and cutting-edge research. In the following
paragraphs, we delve into each of the categories and subcategories.

Figure 3: Proposed Taxonomy of Patient-Centric Knowledge Graph

The first category pertains to construction of a PCKG. It is further divided
into four sub-categories including ontology, knowledge sources, knowledge
extraction, and knowledge representation.

The initial step, Ontology, revolves around designing structured frameworks
that are custom-tailored to meet patient-centered needs, ensuring that the
graph accurately captures the essence of patient data while also adhering to
industry standards and best practices. It is important to articulate
distinctions among closely related terms in this context: a Knowledge Graph
(KG), which represents interconnected real-world facts; a Knowledge Base (KB),
which stores this structured information; and an ontology, which provides a
structured framework that illustrates and organizes concepts, ensuring
consistent understanding and interpretation of the data within the KG and KB.
Second, considering the diverse nature of healthcare data, our Knowledge
Sources segment highlights the differences and integration methods for three
main sources of data: structured, semi-structured, and unstructured data.
Third, the Knowledge Extraction phase employs techniques like Named Entity
Recognition (NER) to pinpoint essential entities such as drugs or symptoms.
The process is complemented by Relationship Extraction (RE), which accurately
maps the relationships among these entities, giving the graph its
characteristic interconnected structure. The last sub-category in construction
is Knowledge Representation, which focuses on detailing the methodological
approach of encapsulating both explicit and implicit knowledge in the PCKG.
This involves defining the schema for entities and relationships, establishing
constraints, and encoding domain knowledge through axioms and rules, thus
constructing a robust framework that effectively translates raw data into a
comprehensible and interconnected knowledge graph that accurately mirrors the
complexity and depth of patient-centric data.

The second category involves the evaluation of the PCKG, emphasizing the need
to assess the efficacy and accuracy of PCKGs post-construction. This
assessment can be facilitated by two main methodologies such as qualitative
assessment and quantitative assessment . The former involves a thorough
examination of the graph to ensure that it adheres to clinical best practices
and contains relevant and correct medical relationships. While the latter
makes use of statistical and computational methodologies to evaluate the
graph’s performance metrics, including accuracy, recall, and precision, and
offers a numerical assessment of its ability to accurately and effectively
represent and infer knowledge. Once the PCKG has been constructed and
evaluated, the next step is its utilization (processing PCKG) to derive
actionable insights. To attain this objective, various approaches are crucial,
including the utilization of methods such as reasoning, semantic search, and
inference. Reasoning enables the PCKG to logically extract new information and
knowledge from the existing knowledge within the graph, providing a basis for
making intelligent decisions and predictions about healthcare outcomes and
strategies. While, Semantic Search allows for structured searches to retrieve
precise information from the graph. Inference, on the other hand, uses the
inherent structure of the graph to derive new insights and conclusions.

The final category of the our taxonomy relates to application of PCKG in
healthcare domain. We explore four primary healthcare use cases, encompassing
recommending individualized interventions, clinical trials, predicting disease
before onset, and others. The first one personalizes patient care treatments
based on insights extracted directly from the graph. While the second
application aims to enhance the efficiency of Clinical Trials, aiding in
developing trial designs or optimizing patient selection. On the other hand, a
particularly innovative application is Predicting Disease Before Onset, where
the complex patterns and relationships mapped in the graph can be harnessed to
proactively detect potential health issues. Although these are highlighted
applications, the potential of PCKGs extends even further, covering areas such
as drug discovery, personalized medication regimens, and preventive health
strategies, to name a few.

Throughout the next sections, we will examine each component of our taxonomy
in more detail in order to gain a better understanding of its significance as
well as provide an overview of the suggested implementation steps.

##  IV Construction, Evaluation and Processing of PCKGs

Throughout this section, we explore the complex processes and methodologies
involved in developing, assessing, and utilizing PCKG. This comprehensive
exploration is structured into four critical categories as illustrated in Fig
3. The first category, construction of PCKG, explains the complex process of
building PCKG, highlighting the significance of ontology, various data
sources, knowledge extraction techniques, and knowledge representation. Next,
evaluation covers both qualitative and quantitative methods for assessing the
efficacy and accuracy of these graphs. Similarly, we discuss advanced
techniques like reasoning, semantic search, and inference, which are essential
to harnessing the full potential of PCKGs to derive actionable insights and
inform healthcare decisions.

###  IV-A PCKG Construction

The process of creating PCKGs involves four steps. To begin with, the ontology
phase involves developing structured frameworks based on patient data, which
ensure accuracy and adherence to standards. Knowledge sources is the second
step, which addresses the collection and integration of diverse types of
healthcare data, including structured, semi-structured, and unstructured data.
During the third step, knowledge extraction, key data entities and their
interconnections are mapped using named entity recognition and relationship
extraction techniques. Lastly, knowledge epresentation involves defining a
schema for entities and relationships, setting constraints, and encoding
domain knowledge to transform raw data into a coherent, interconnected graph
reflecting patient data complexity. In the following subsections, we will
explore each of these steps in detail.

####  IV-A1 Ontology

Ontologies provide a structured framework for representing knowledge, enabling
the integration of diverse data sources and facilitating sophisticated access
to health information, which is crucial in patient-centered healthcare [38].
The design and formalization of ontologies involve creating a comprehensive
schema that integrates information from various structured and unstructured
data sources [39]. Ontologies are different from knowledge bases and schemas.
While an ontology is a formal representation of a set of concepts within a
domain and the relationships between those concepts, a knowledge base is a
collection of knowledge in a specific area, often built upon an ontology. A
schema, on the other hand, is more about the organization of data as a
blueprint of how the database is constructed, without delving into the domain-
specific relationships and properties that an ontology provides. The
application of ontologies in healthcare has been demonstrated in the body of
literature. For instance, Sun et al. [40] presented an ontology-enabled
healthcare service provision model that facilitates joint referral decisions
between patients and general practitioners, promoting patient-centric
healthcare service provision. The following subsections will expand more on
the design and formalization of ontologies.

##### Design

The growing digitization of healthcare data and the proliferation of medical
information need effective and structured methods of representing and managing
patient-centric knowledge. Ontologies as formal representations of knowledge,
play a critical role in supporting interoperability, data integration, and
decision support [41], in healthcare systems. In the context of PCKGs,
ontology design is a fundamental process that involves defining and modeling
the important entities, connections, and features related to patient health
and treatment. Figure 4 provides an example of a patient’s ontology. Authors
in [42] argue that the rationale behind ontology development is to aid in a
shared understanding of information structure among people or software agents,
facilitate the reuse of domain knowledge, make domain assumptions explicit,
separate domain knowledge from operational knowledge, and analyze domain
knowledge. However, there is not standard methodology [43] or no single
correct way of formulating ontology [42].

Figure 4: Illustration of a Basic Patient’s Ontology

The initial stage in ontology design is to establish the scope and domain of
the PCKG. The scope of the ontology specifies the breadth of medical knowledge
included, whereas the domain defines the precise areas of concentration, such
as general medical information, specific diseases, or healthcare procedures.
Identifying the domain aids in the selection of acceptable current ontologies
such as SNOMED-CT [44], RxNorm [45], and LOINC [46] or healthcare standards
that can be utilized to maintain interoperability and consistency. While
current ontologies and standards may provide a solid foundation, some patient-
centric concepts may not be fully covered. In such circumstances, additional
classes and features must be added to cater to specific requirements. This
requires collaboration between domain experts and ontology engineers to
guarantee that the ontological representations are consistent with healthcare
practices and guidelines.

The second step is to define the core entities. Entities such as _Patient_ ,
_Disease_ , _Medication_ ,_Symptom_ , _Medical Procedure_ , _Healthcare
Provider_ , etc. are typically included. These entities serve as the building
blocks for constructing the KG and are crucial for capturing patient-related
information.

Next step is to extract relationship or specifying relationship between
entities. This step is important for capturing the complex interactions within
patient-centric healthcare. Relationships, such as _Diagnosis, Treats, Has
Symptom, and Undergoes,_ enable meaningful associations and support
inferencing capabilities. The developer of ontology during this stage should
also carefully consider associated attributes and properties of the entities.
For example, attributes of _patient_ could be _age_ , _gender_ , _sex_ ,
_demography_ etc. These attributes can influence the diagnosis and treatment
decisions [47].

##### Formalization

Formalizing the ontology focuses on structuring and representing patient data
and medical knowledge in a way that is understandable and accessible to
healthcare systems. Puustjarvi et al. [38] leveraged the Resource Description
Framework (RDF) and Web Ontology Language (OWL), to facilitate sophisticated
access to health information, a cornerstone for effective patient care. This
innovative approach underscores the evolving landscape of healthcare
informatics, where the structuring and accessibility of patient data are
extremely important.Similarly, Jiang et al. [48] provided insight into the
role of Formal Concept Analysis (FCA) in the development of ontologies within
clinical domains. FCA emerges as a key tool, offering linguistic and context-
based knowledge. This knowledge is indispensable for clinical experts, aiding
them in comprehending and applying ontology effectively in their practice.
Integrating FCA into ontology development signifies a deeper understanding of
clinical data and its nuances, enhancing the overall utility of these
ontologies in real-world medical settings.

Further broadening this scope, Djedidi and Aufaure [49] presented a
methodology that underscores the integration of heterogeneous data sources
with existing ontologies and standards. This approach is instrumental in
constructing a medical domain ontology as a comprehensive, knowledge-centric
decision support system. Such integration is crucial in ensuring the developed
ontology is robust and aligns seamlessly with existing medical knowledge
frameworks and data sources. Likewise, Mendes, Rodrigues, and Baeta [50] made
significant contributions to the development of the Ontology for General
Clinical Practice (OGCP). This ontology extends the Ontology for General
Medical Science (OGMS) by incorporating the Clinical Patient Record (CPR)
structure. This extension significantly enhances the representation and
reasoning capabilities within clinical practice knowledge, especially in the
context of natural language text. The OGCP stands as a testament to the
evolving complexity and sophistication required in modern medical ontologies,
catering to the nuanced needs of clinical practice.

The dynamic nature of medical knowledge and patient data necessitates
methodologies for tracking and updating information. In this context, Ognyanov
and Kiryakov [51] proposed a formal model for tracking changes in RDF(S)
repositories. This model is vital for maintaining the accuracy and relevance
of the KG over time, addressing the ever-changing landscape of medical data
and knowledge. Addressing the user perspective, Clarkson et al. [52]
introduced a user-centric methodology for the ontology population. This
approach aligns user concepts with target ontologies, proving an efficient
method for building and maintaining ontologies across various domains. The
user-centric approach ensures that the developed ontologies are technically
sound and resonate with the needs and understandings of the end-users, be they
patients or healthcare professionals.

To bridge the gap between formal ontology and practical application, Ferilli
[53] proposed an intermediate format that can be easily mapped onto formal
ontology for complex reasoning and a graph database for efficient data
handling. This innovation represents a significant stride in harmonizing the
theoretical aspects of ontology with the practical demands of data management,
ensuring that the developed ontologies are both conceptually sound and
practically applicable.

Together, these diverse yet interconnected research efforts paint a
comprehensive picture of ontology formalization’s current state and future
potential in patient-centric healthcare. They highlight a collaborative and
multi-faceted approach to developing technically robust ontologies that are
deeply integrated with the practical realities of healthcare delivery.

####  IV-A2 Knowledge Sources

PCKGs are complex structures that integrate diverse data sources, as
illustrated in Fig 5, in order to offer a comprehensive perspective on a
patient’s medical past, present ailments, and prospective therapies. The
extent of the depth of a PCKG is contingent upon the diversity and quality of
the data sources that contribute to its composition. The sources can be
classified into three main categories: structured, semi-structured, and
unstructured data.

Figure 5: Diverse Sources of Medical and Healthcare Data

##### Structured Data

Structured data is organized and easily searchable in relational databases. It
follows a specific schema or model, making it straightforward to query and
analyze. Structured healthcare data, integral to PCKGs, is exemplified by
EHRs, lab results and dagnostics, and genomic and molecular data. EHRs, as
digital records of a patient’s medical history, encompass diagnoses,
medications, treatment plans, and vital statistics, providing a highly
structured backbone for PCKGs. Transitioning to lab results and diagnostics,
this category includes structured, numerical, or categorical data from blood
tests, imaging studies (like X-rays, MRIs), and other diagnostic tests,
facilitating their integration into PCKGs. Lastly, genomic and molecular data,
derived from genetic tests, offer structured insights into a patient’s disease
predisposition, proving essential for the advancement of personalized
medicine. Collectively, these structured data forms are key in healthcare
analytics and personalized treatment planning.

##### Semi-Structured Data

Semi-structured data does not reside in a relational database but does have
some organizational properties that make it easier to analyze. It often
requires some level of transformation to be fully utilized. Semi-Structured
healthcare data encompassesclinical notes and patient-generated data, each
possessing unique characteristics that bridge the gap between structured and
unstructured data. Clinical notes and narratives, while fundamentally
unstructured in text, often adhere to templates or contain tags, rendering
them semi-structured and providing contextual depth absent in purely
structured datasets. Similarly, patient-generated data, sourced from wearables
like fitness trackers and mobile health apps, presents structured data points
(e.g., heart rate, steps) but lacks a consistent schema overall, contributing
to the semi-structured nature of the data set. This combination of structured
elements within an otherwise unstructured framework is valuable for
comprehensive healthcare data analysis.

##### Unstructured Data

Unstructured data is information that doesn’t have a pre-defined data model,
making it challenging to analyze using conventional methods. Unstructured
healthcare data, characterized by its non-standardized format, includes
patient surveys and interviews as well as medical literature and research
papers, each offering unique challenges and insights for PCKGs. Patient
surveys and interviews, typically in text or audio formats, yield qualitative
insights into patients’ conditions but are unstructured, necessitating
advanced analytics for effective integration into PCKGs. Similarly, medical
literature and research papers are a rich source of valuable insights but
remain unstructured; thus, they require NLP techniques to distill and extract
relevant information for PCKG incorporation. This unstructured data, with its
inherent complexity, plays a crucial role in enhancing the depth and breadth
of healthcare analytics and patient care understanding.

By integrating these diverse data sources, PCKGs can offer a multi-dimensional
view of a patient’s health, thereby enabling more personalized and effective
healthcare interventions.

####  IV-A3 Knowledge Extraction

In the process of constructing PCKGs, knowledge extraction serves a pivotal
role. This stage leverages techniques such as Named Entity Recognition (NER)
and Relation Extraction (RE) to distill valuable insights from unstructured
data. While NER is focused on identifying and categorizing key entities
present in the data, RE takes a step further to identify the relationships
that exist between these entities. Through the harmonious interplay of NER and
RE, a structured and rich KG can be constructed, paving the way for more
personalized and efficient healthcare solutions.

##### Named Entity Recognition (NER)

NER involves the identification and classification of entities such as
diseases, symptoms, and medications from unstructured text. This process is
fundamental in transforming raw data into structured, actionable information.
Recent advancements in NER methodologies have significantly enhanced the
development of KGs, particularly in the medical domain. For instance, Li et
al. [54] introduced the BiLSTM-Att-CRF model, which integrates an attention
mechanism and part-of-speech features to improve clinical NER in Chinese
electronic medical records. Building on this, Keretna, Lim, and Creighton [55]
proposed a graph-based technique that enhances medical NER performance by up
to 26% in unstructured medical text, showcasing the versatility of NER
applications.

Furthering these developments, Wang and He [56] developed a bi-directional
joint embedding model that combines encyclopedic knowledge with original text,
thereby showing improved results in Chinese medical NER. Complementing this
approach, Wang, Xia, Zhou, Ruan, Gao, and He [57] demonstrated that
incorporating dictionaries into deep neural networks effectively addresses the
challenge of rare and unseen entities in clinical NER, thus broadening the
scope of NER applications. In a similar vein, Guan and Tezuka [58] highlighted
the importance of entity linking and intent recognition in medical question-
answering systems, which significantly improves the efficiency and accuracy of
medical KG searches. In a similar context, Zhou, Cai, Zhang, Guo, and Yuan
[59] developed a multi-task adversarial active learning model that enhances
medical NER and normalization by considering task-specific features, further
illustrating the dynamic evolution of NER methodologies.

These diverse methodologies reflect the evolving landscape of NER in the
development of PCKGs. The integration of advanced machine learning techniques,
attention mechanisms, and domain-specific knowledge has led to more accurate
and efficient entity recognition, which is crucial for the effective
utilization of KGs in healthcare.

##### Relation Extraction (RE)

Similar to NER, RE is a fundamental process in transforming unstructured data
into a structured form, enabling more effective data utilization in healthcare
and medical research. Recent advancements in RE methodologies, particularly in
the context of PCKGs, have been significant. Ruan et al. [60] demonstrated
that a multi-view graph learning method could notably enhance the precision,
recall, and F1-score in relation extraction from Chinese clinical records,
outperforming state-of-the-art methods. Similarly, Li et al. [61] highlighted
the evolution of relation extraction algorithms, emphasizing the role of deep
learning, reinforcement learning, active learning, and transfer learning in
this domain.

In the realm of therapy-disease KGs, Wang et al. [62] constructed a Therapy-
Disease KG (TDKG) using entity relationship extraction, achieving an 88.98%
accuracy in extracting valid relationships from treatment-disease literature.
This underscores the potential of RE in enhancing the accuracy and
comprehensiveness of medical KGs. Furthermore, the integration of KGs and
lexical features in biomedical relation extraction, as shown by Zhao et al.
[63], indicates significant improvements in semantic understanding. This
approach exemplifies the innovative strategies adopted in recent research to
enhance the effectiveness of RE in medical contexts. The development of
Conditional Random Field (CRF)-powered classification models with deep
learning for clinical relation extraction, as demonstrated by Lv et al. [64],
further illustrates the ongoing advancements in this field. These models have
shown effectiveness in extracting clinical relation information from medical
texts, thereby improving the construction of medical KGs.

The field of RE in PCKGs is rapidly evolving, with methodologies leveraging
multi-view graph learning, deep learning, and knowledge-enhanced approaches.
These advancements are crucial in extracting meaningful and accurate
relationships from complex medical texts, thereby enriching the KGs that are
essential for advancing patient-centered care and medical research.

####  IV-A4 Knowledge Representation

Knowledge Representation (KR) serves as a methodological backbone, enabling
the systematic transformation of both explicit and implicit knowledge into a
structured and interpretable format. This transformation is crucial for
machines to effectively process, analyze, and infer from the data, thereby
bridging the gap between raw data and actionable insights.

The process of KR encompasses four key components such as schema definition,
constraint establishment, domain knowledge encoding and axioms and rules
utilization as explained in Section III. Firstly, schema definition involves
outlining the structure of entities and their relationships within the KG.
This step is fundamental in organizing data in a meaningful and accessible
manner. Secondly, constraint establishment is essential for maintaining data
integrity and consistency, ensuring that the relationships and entities adhere
to predefined rules and norms. Thirdly, incorporating domain knowledge is
instrumental in embedding domain-specific intelligence into the system. This
allows for a nuanced understanding of the specific area of interest. Fourthly,
the integration of axioms and rules facilitates advanced reasoning
capabilities, enabling the system to infer new knowledge and insights based on
the existing data.

These components collectively form a robust framework that simplifies complex
data and enhances the system’s capability to mirror the intricacies inherent
in patient-centric data. In the following subsections, we will delve deeper
into each of these components, exploring their roles and significance in the
broader context of KR.

##### Schema Definition

The concept of schema definition in PCKGs revolves around structuring and
organizing patient-related data in a way that enhances understanding and
interaction with this information. Schemas in KGs serve as blueprints for
organizing data, enabling more effective data integration, querying, and
analysis. This is particularly crucial in healthcare, where patient data is
complex and multifaceted. It aims to create a unified and comprehensive view
of patient information, facilitating better healthcare outcomes through
informed decision-making. Ghosh and Gilboa [65] emphasize the role of memory
schemas in coordinating knowledge structures, highlighting the importance of
understanding cognitive processes in schema development. This perspective is
crucial in PCKGs, where patient data must be organized in a way that aligns
with healthcare professionals’ cognitive schemas.

Building on the concept of visual schemas by Kranjec et al.[66], which enhance
comprehension and aid in the diagnostic process, the work of Ohira et al. [67]
takes a step further by introducing the dynamic nature of graph-based data
models. This flexibility is key in the realm of PCKGs, where patient data is
not static but continuously evolving. The ability to dynamically add and
remove relationships in these KGs ensures that they remain up-to-date and
reflective of the latest patient information, a critical aspect in the fast-
paced environment of healthcare. Transitioning from the dynamic structuring of
patient data, the focus shifts to the cognitive aspects of PCKGs as discussed
by Gilboa and Marlatte [68]. Their research underscores the significance of
aligning PCKGs with the mental models of healthcare professionals. This
alignment is not just about structuring data efficiently; it’s about ensuring
that the KG resonates with the users’ cognitive processes, thereby enhancing
memory recall and decision-making capabilities.

Further advancing this discussion, Ji et al. [69] delve into the intricacies
of schema induction in KGs. Their exploration into the challenges and
developments in this field is particularly relevant for PCKGs. As these KGs
become more complex and integral to healthcare, understanding and overcoming
the challenges in schema induction is paramount for ensuring that PCKGs are
robust, comprehensive, and seamlessly integrated into the healthcare workflow.

##### Constraints Establishment

The establishment of constraints in PCKGs focuses on enhancing the accuracy,
privacy, and efficiency of medical data management and analysis. Various
methodologies and strategies adopted in the creation and application of
constraints in PCKGs, as evidenced by recent academic research. For example,
Aguilar-Escobar et al. [70] demonstrate the application of the Theory of
Constraints (TOC) in improving the logistics of medical records in hospitals,
highlighting the potential of TOC in enhancing service quality and reducing
costs in healthcare settings. This approach underscores the importance of
efficient data management in PCKGs.

Building on this notion of efficiency, the focus shifts to the critical aspect
of privacy and security in healthcare, where Mathew and Obradovic [71]
illustrate how constraint graphs can secure privacy in medical transactions,
preventing unauthorized attribute-based transformations in clinical data. This
is crucial in PCKGs, where patient data sensitivity is paramount. The
evolution of PCKGs further extends into the domain of dynamic data handling
and adaptability. Matsuto and Shiina [72] propose a model for medical
knowledge representation based on constraints, which dynamically generates
laboratory schedules using constraint propagation. This approach emphasizes
the adaptability of PCKGs in accommodating patient-specific information.

The adaptability of PCKGs is further enhanced by advancements in KG reasoning.
Wu et al. [73] discuss a constraint-based embedding model for KG reasoning,
focusing on semantic-type constraints in constructing corrupted triplets. This
methodology significantly improves the reasoning accuracy of KGs, a key aspect
in developing effective PCKGs. Alongside reasoning accuracy, the
representation of knowledge and uncertainties also plays an important role.
Pugh and Gillan [74] introduce Propositional Constraint Graphs (PCG) as a tool
for representing knowledge and uncertainties in various tasks, including
healthcare. This approach aids in the clear visualization and management of
complex information in PCKGs.

The application of these concepts is not limited to static scenarios but also
extends to dynamic and evolving medical challenges. Tu [75] proposes a
constraint propagation approach for identifying biological pathways in
COVID-19 KGs. This method, utilizing semantically extracted information, is
indicative of the potential for PCKGs in pandemic response and other rapidly
evolving medical scenarios. Finally, the integration of context-aware
constraints brings a new dimension to the integrity and validation of
knowledge in PCKGs. Wilcke et al. [76] discuss the use of context-aware
constraints to improve knowledge integrity in heterogeneous KGs. This balance
of complexity and utility is crucial for knowledge validation tasks in PCKGs
[76], highlighting the continuous evolution and application of constraints in
the development of robust and efficient PCKGs.

The establishment of constraints in PCKGs is a multifaceted process, involving
the integration of various methodologies to enhance data accuracy, privacy,
and efficiency. The strategies adopted in recent research works reflect a
growing emphasis on adaptability, security, and precision in managing patient-
centric data in healthcare systems.

##### Domain Knowledge Encoding

Domain Knowledge encoding focuses on the integration and representation of
complex medical knowledge in a structured and interconnected format. This
encoding process is crucial for developing PCKGs, which are instrumental in
enhancing patient care through personalized and informed decision-making. The
methodologies for creating domain knowledge encoding in PCKGs are diverse and
innovative. Wang et al. [77] proposed a framework utilizing medical chart and
note data, employing a bag-of-words encoding method and a model that considers
both global information and local correlations between diseases. This approach
underscores the importance of capturing the nuances of medical data for
effective KG construction.

Further expanding on these methodologies, Ji et al. [78] highlighted the
significance of representation space, scoring function, encoding models, and
auxiliary information in KG creation. This comprehensive approach indicates
the multi-faceted nature of KG development, where various components
contribute to the robustness and accuracy of the resulting graph. Similarly,
Yuan et al. [79] presented an approach for biomedical domain KG construction
that includes entity recognition, unsupervised entity and relation embedding,
latent relation generation, clustering, relation refinement, and relation
assignment. This method demonstrates the complexity involved in accurately
representing medical knowledge in graph form. Rotmensch et al. [28] on the
other hand, utilized rudimentary concept extraction and three probabilistic
models to construct high-quality health KGs, with the Noisy OR model yielding
the best results. This study exemplifies the use of probabilistic models in
enhancing the quality of KGs. Furthermore, Yu et al. [80] proposed a
relationship extraction method for domain KG construction, obtaining upper and
lower relationships from structured data, semi-structured data, and
unstructured text. This method highlights the importance of extracting
relationships from diverse data sources to enrich the KG.

These methodologies contribute to the construction of comprehensive and
accurate KGs. In addition, they also pave the way for innovative applications
in patient-centric healthcare.

##### Axioms and Rules Utilization

PCKGs utilize axioms and rules to enhance the representation and analysis of
patient data, leading to more informed healthcare decisions. Axioms in PCKGs
are fundamental principles or statements accepted as true without proof, used
to define relationships and properties within the graph. Rules, on the other
hand, are logical statements that infer new knowledge from existing data
within the graph. This combination of axioms and rules plays an important role
in structuring and interpreting complex medical data, enabling more
personalized and effective patient care.

The methodologies for creating axioms and rules in PCKGs vary across different
research works. Chu et al. [81] demonstrated the adaptation of multi-center
clinical datasets by incorporating an external KG, which enhanced patient
features and improved predictions for acute kidney injury in heart failure
patients. This approach signifies the importance of integrating diverse data
sources and knowledge bases into PCKGs. In another study, Tomczak and
Gonczarek [82] developed a Graph-based Rules Inducer for extracting decision
rules from data streams in diabetes treatment. This method supported medical
interviews by tracking hidden context changes and avoiding overfitting,
highlighting the importance of dynamic rule adaptation in response to evolving
data. Shang et al. [83] utilized an EHR-oriented KG system to effectively
harness non-used information buried in EHRs, thereby improving healthcare
quality and providing interpretable recommendations for specialist physicians.

The use of graph-based association rules for mining medical databases, as
demonstrated by Alzoubi [84], revealed the potential of discovering hidden
medical knowledge, which could contribute significantly to understanding
complex medical conditions like preterm birth. Similarly, RuleHub, as
introduced by Ahmadi et al. [85], is an extensible corpus of rules for public
KGs, enabling users to archive and retrieve rules from popular KGs. This tool
improves data understanding and reduces redundant work, illustrating the
importance of rule sharing and standardization in the field. Wright et al.
[86] developed a set of rules for inferring patient problems from clinical and
billing data, which performed better than using problem list or billing data
alone. This method improved clinical decision support and care improvement,
showcasing the practical application of rule-based systems in clinical
settings.

Based on our review, the utilization of axioms and rules in PCKGs is a dynamic
and evolving field, with methodologies ranging from the integration of diverse
data sources to the development of specialized, disease-centric graphs. These
approaches enhance the understanding of complex medical data and significantly
contribute to the advancement of personalized patient care.

###  IV-B PCKG Evaluation

Evaluation of PCKGs is critical for ensuring their efficacy and accuracy in
representing and inferring medical knowledge. These evaluations are typically
conducted using two main methodologies: qualitative and quantitative
assessments. Qualitative assessment involves a detailed examination of the KG
to ensure it aligns with clinical best practices and accurately reflects
medical relationships. This method may usability studies, content analysis,
and expert panel reviews to gauge the graph’s relevance and correctness[28]
[87]. On the other hand, quantitative assessment employs statistical and
computational techniques to measure performance metrics such as accuracy,
recall, and precision, providing a numerical evaluation of the graph’s
effectiveness[28].

####  IV-B1 Quantitative Assessment

Quantitative methodologies for PCKG evaluation focus on numerical metrics to
assess the graph’s performance. These methods include completeness,
consistency, accuracy, and embedding techniques.

Completeness in a KG refers to the extent to which all necessary information
is represented. In healthcare, this means ensuring that a PCKG includes
comprehensive data on diseases, symptoms, treatments, and patient histories. A
systematic literature review by Issa et al. [88] emphasizes the importance of
assessing the completeness of KGs, identifying various methodologies and
metrics used for this purpose. On the other hand, consistency involves
checking for logical coherence within the graph, particularly in terms of
medical terminologies and relationships. This ensures that the KG does not
contain contradictory information, which is crucial for clinical decision-
making. The work of Sharma and Bhatt [89] on privacy-preserving KGs in
healthcare highlights the importance of maintaining consistency in data
representation.

Other methods like accuracy assessment and embedding techniques are integral
to the development of reliable PCKGs. The accuracy of a KG, as highlighted by
Li et al. [90], is crucial for patient safety and effective treatment
planning, ensuring that the information aligns with authoritative medical
databases and literature. Complementing this, embedding techniques like
node2vec or GraphSAGE, as demonstrated by Talukder et al. [91], play a vital
role in transforming complex graph data into a more accessible format, thereby
enhancing the graph’s ability to accurately capture and represent semantic
relationships in healthcare applications.

####  IV-B2 Qualitative Assessment

Qualitative assessment methodologies are instrumental in evaluating the
effectiveness of PCKGs in meeting the needs of their intended users, including
clinicians, researchers, and patients.

In the qualitative assessment of PCKGs, usability studies and feedback loops
play an important role in the KG evaluation. Usability studies, as highlighted
by Brundage et al. [87], are crucial for evaluating the ease of use and
accessibility of PCKGs, particularly in the context of interpreting patient-
reported outcomes (PROs) in graphic format. These studies typically employ a
blend of qualitative and quantitative methods, including surveys, interviews,
and user testing, to gather comprehensive feedback on the user experience.
Complementing this, feedback loops, as discussed by Rospocher et al. [92],
enable users to provide direct input on their experiences with the KG. This
feedback is instrumental for the continuous refinement and adaptation of the
KG, ensuring it aligns closely with user needs and preferences.

Furthermore, the quality of PCKGs is also gauged through comparison with
established databases and expert validation. Benchmarking PCKGs against
renowned medical databases like PubMed and ClinicalTrials.gov is essential for
assessing their content coverage and accuracy [93][94]. This comparison helps
in identifying any gaps or discrepancies in the PCKG. Additionally, expert
validation, underscored by the work of Rotmensch et al. [28], involves domain
experts who ensure the medical relevance and accuracy of the PCKG’s content.
Their insights into the clinical applicability of the information within the
KG are vital for maintaining its reliability and trustworthiness.

The evaluation of PCKGs through qualitative and quantitative assessments is a
multifaceted process that involves both the review of medical content and the
analysis of performance metrics. Medical professionals and patients have
benefited from progress in this field by ensuring that PCKGs serve as reliable
and effective tools.

###  IV-C PCKG Processing

By processing PCKGs, healthcare providers can extract actionable insights that
inform clinical decision. The utilization of PCKGs involve different methods
including reasoning, semantic search, and inference.

####  IV-C1 Reasoning

KG Reasoning in healthcare extends beyond mere data aggregation, playing an
important role in deriving new insights and facilitating informed decision-
making. This reasoning process involves structuring information, extracting
features and relations, and performing logical deductions to uncover new
knowledge from existing data within the graph [95]. For instance, in the
context of mental healthcare, KGs have been utilized for emotion recognition
from facial expressions and heart rate, demonstrating the potential of
knowledge reasoning in predicting emotional states [96]. Moreover, KGs in
healthcare support clinical decision-making and enhance hospital efficiency by
integrating heterogeneous medical knowledge and services, thereby enabling
cognitive computing and semantic reasoning [97].

####  IV-C2 Semantic Search

It enables the retrieval of specific information by structuring searches to
navigate the complex relationships within the graph. Various strategies have
been adopted to enhance the precision and efficiency of information retrieval
in PCKGs. For instance, the use of Semantic Web and Knowledge Management
approaches has been pivotal in implementing patient-centric strategies with
well-defined semantics[98]. Graph-based methods that incorporate semantic-rich
knowledge bases and lazy learning algorithms have shown promise in linking
multimodal clinical data for improved diagnosis performance [99]. Moreover,
the retrieval of similar clinical cases has been refined by mapping text to
Unified Medical Language System (UMLS) concepts and representing patient
records as semantic graphs, demonstrating superiority over traditional
models[100].

####  IV-C3 Inference

Inference uses the graph’s inherent structure to deduce new insights,
facilitating the discovery of patterns and trends that may not be immediately
apparent[99][98]. These methods underscore the advances in accuracy and
efficiency in the processing and utilization of PCKGs, leading to enhanced
healthcare outcomes.

After examining the methodologies for building, evaluating, and processing
PCKGs, we move on to practical applications and use cases that will
demonstrate how PCKGs can be applied in real-world scenarios, demonstrating
their impact and significance in healthcare.

##  V Applications & Use cases

As the healthcare industry increasingly embraces data-driven decision-making,
PCKGs have emerged as a powerful tool for personalized medicine. These KGs,
which place the patient at the center of a complex network of interconnected
health data, offer a holistic view of a patient’s health journey. They
encompass various data, from medical history and genetic information to
lifestyle factors and environmental influences. The applications of these KGs
are broad and innovative, offering the potential for improved disease
prediction, enhanced treatment planning, and more effective preventive
strategies. In the following subsections, we will explore these applications
in detail, shedding light on how PCKGs revolutionize healthcare and pave the
way for a more personalized, predictive, and proactive approach to patient
care.

###  V-A Predicting Disease Before Onset

The healthcare sector has seen significant advancements in patient-centric
solutions by integrating KGs. Using a KG to proactively forecast the
likelihood of a disease developing in an individual before any symptoms appear
involves synthesizing various data points related to patient history, genetic
information, lifestyle factors, and broader medical knowledge to identify
patterns and risk factors associated with diseases. This predictive model
allows for early intervention strategies, significantly altering the disease
trajectory and improving the patients’ long-term health outcomes.

The methodologies for creating PCKGs for disease prediction are diverse and
innovative. Chen et al. [25] discuss the robust extraction of medical
knowledge from EHRs to build graphs that evaluate accuracy across different
diseases and patient demographics using non-linear functions for causal
relationships. Similarly, Talukder et al. [91] describe an AI-based approach
integrating disease-related knowledge bodies with Node2VEC for link prediction
in disease-symptom networks. Furthermore, Liang et al. [101] highlight the use
of multi-hop reasoning over KGs, which provides interpretability and is
superior to single-hop methods. In similar vein, the refinement of medical KGs
using latent representations aids in prediction and maintains the
explainability of diagnoses [102]. Expanding on these advancements, Li et al.
[103] introduce a Graph Neural Network-Based Diagnosis Prediction (GNDP) model
that uses spatial-temporal graph convolutional networks for diagnosis
predictions. In a complementary manner, Zheng et al. [104] proposed a Multi-
modal Graph Learning framework (MMGL) that exploits multi-modality information
for disease prediction.

Different strategies across research works include the use of random walk
along KG [105], Graph Neural Networks (GNN) for embedding medical concepts
[106], and hybrid systems combining KGs with clinical experience for pediatric
disease prediction [107]. Zhang et. al [108] built an automatic question-
answering system based on medical KGs, while Saha et al. [109] predicted
missing and noisy links in clinical KGs using neighborhood-based embeddings.
Key findings from our literature review indicate significant advances in
disease prediction accuracy, efficiency, and outcomes. For instance, the
integration of GNNs with EMR data has led to highly representative node
embeddings that improve prediction accuracy [106] . The use of tensor
factorization on biological KGs for predicting co-morbid disease pairs [110]
and the construction of KGs based on evidence-based medicine for diabetes
complications [111] are notable advances.

PCKGs represent a significant step forward in the field of disease prediction.
They offer the potential to transform patient outcomes by enabling early
detection and personalized treatment plans. Future implications include the
continued refinement of these models to enhance their predictive power and the
integration of even more diverse data sources to capture the full spectrum of
patient health and disease progression. In Table II we summarize a list of
selected disease prediction applications in terms of their impact and
limitations in the field of PCKGs.

TABLE II: A summary of selected literature on using PCKGs for “Predicting Disease Before Onset” Paper Title |  Focus/Objective |  Contribution(s) |  Limitation(s)
---|---|---|---
Predicting Disease Before Onset
A novel link prediction approach on clinical knowledge graphs utilizing graph structures [112] |  The goal is to create a proof of concept showcasing the efficacy of graph structures in AI methodologies. |  • Development of a graph-based method merging Conditional Random Fields (CRFs) and graph embedding for knowledge discovery. • This method successfully predicts labels for graph nodes with high precision and recall. |  • The method is time-intensive when querying features from the graph, particularly with large datasets. • Increased runtime and memory demands for multi-node paths in the graph.
Deep knowledge reasoning guided disease prediction [101] |  The paper aims to enhance model interpretability by integrating knowledge entities with single-hop and multi-hop relationships |  • Introduction of HitaNet, a hierarchical time-aware attention network. • HitaNet uses a self-attention based transformer model for enhanced disease prediction. • The paper presents a unique token wrapping method to merge knowledge graph insights with EHR data. |  • Lower time efficiency compared to baseline methods as a limitation. • The proposed method’s effectiveness diminishes with the availability of sufficient data.
Predicting missing and noisy links via neighbourhood preserving graph embeddings in a clinical knowlegebase [109] |  The paper proposes a model that combines support vector classification (SVC) and neural network-based probabilistic embedding (NPE) to predict the links between clinical entities in the KG |  • The development of a novel approach that integrates SVC and NPE for link prediction in KGs. • The model achieved promising results in terms of predicting missing associations. |  • The reliance on existing clinical databases, which may contain noisy or incomplete data. • The potential mismatch between model predictions and clinical recommendations.
Relation prediction of co-morbid diseases using knowledge graph completion [110] |  The objective is to predict relationships between co-morbid diseases using knowledge graph completion techniques |  • Proposing a method that combines distributed representation learning and graph embedding to predict disease-disease relationships • Development of a novel approach that leverages the structure and semantics of a KG to predict disease relationships |  • The proposed method relies on the availability of a comprehensive and accurate knowledge graph. • The evaluation of the method is limited to a specific dataset.
Disease prediction via graph neural networks [106] |  The paper addresses the challenges in predicting both common and rare diseases by integrating expert knowledge with machine learning techniques. |  • Proposing a systematic solution that combines expert knowledge with machine learning. • Introducing a novel graph embedding-based model for disease prediction, which learns embeddings from medical concept graphs and patient record graphs. |  • Struggling to adapt to rare diseases due to data scarcity and complex symptom-diagnosis relationships. • Reliance on historical patient records for model training limits the ability to serve new patients.

###  V-B Recommending Individualized Interventions

Intervention recommendation aims to use KGs to suggest tailored medications
and treatments. By mapping patients’ unique health profiles, these graphs
enable physicians to pinpoint optimal therapies that enhance the precision of
medical decisions, leading to improved patient outcomes. The application of
KGs in personalized treatments is predicated on the integration of diverse
data sources, including EHRs, clinical notes, and patient-generated data, to
construct a comprehensive, interconnected data structure that reflects
individual patient profiles. This patient-centric approach is crucial as it
allows for treatments to be tailored based on the unique medical history,
genetic information, lifestyle, and preferences of each patient, which is a
departure from the one-size-fits-all healthcare model.

The approaches to developing and implementing PCKGs differ among research
studies, but they all aim to achieve robustness, accuracy, and
personalization. For instance, Gyrard et al. [113] aggregate knowledge from
IoT devices, clinical notes, and EMRs to manage chronic diseases, showcasing
the integration of AI and machine learning in constructing Personalized
Healthcare Knowledge Graphs (PHKGs). Transitioning from a general approach to
a more specialized application, Individualized Knowledge Graphs (IKGs) in
cardiovascular medicine are one strategy for developing these KGs, which
combine biological knowledge with medical histories and health outcomes to
create personalized treatment strategies [114]. Further refining the
methodological framework, Rotmensch et al. [28] utilized concept extraction
and probabilistic models, finding the Noisy OR model particularly effective
for constructing high-quality health KGs. Building on these methodologies,
Shirai, et al. [4] applied Personal Knowledge Graphs (PKGs) to integrate
patient-specific information into decision-making tools for personalized
healthcare. In a similar vein of enhancing disease treatment strategies
through personalized data, Zhu et al. [115] introduced a KG that enhances rare
disease (RD) treatment recommendations by systematically compiling and
semantically annotating RD-related scientific articles, aggregating essential
research findings and therapeutic insights with a sophisticated data model.

Literature has demonstrated significant advances in treatment accuracy,
efficiency, and outcomes. For instance, the use of Four-Tuple Path Matrix in
Traditional Chinese Medicine has been proposed to create personalized KGs,
enhancing diagnostic modalities [116]. Li et al. [117] demonstrated how
personal KGs could be automatically constructed from user utterances in
conversational dialogs, indicating the potential for real-time, dynamic
treatment adjustments. PCKGs provide multiple advantages. By analyzing patient
profiles using KGs, treatments are more accurate and efficient than
traditional methods. For example, Zhang et al. [118] developed an intuitive
graph representation of knowledge for nonpharmacological treatment of
psychotic symptoms in dementia, which could potentially transform care
strategies for such complex conditions.

There is a wide range of potential implications for future personalized
treatment and patient outcomes. A more informed and dynamic approach to
treatment has the potential to enhance medical precision, improve patient
engagement, and optimize health outcomes by integrating PCKGs. It is expected
that these methods will become more sophisticated as the field evolves,
allowing for even greater personalization and efficacy in treatment. In Table
III we summarize a list of selected treatment decision applications in terms
of their impact and limitations in the field of PCKGs.

TABLE III: A summary of selected literature on using PCKGs for “Recommending Individualized Intervention” Paper Title |  Focus/Objective |  Contribution(s) |  Limitation(s)
---|---|---|---
Recommending Individualized Interventions
Individualized Knowledge Graph [114] |  • Envisioning individualized Knowledge Graphs (iKGs) in cardiovascular medicine. • Proposing a modern informatics platform for transforming clinical and scientific discovery. |  • Introducing the concept of iKGs for aggregating and presenting individualized cardiovascular health data. • Highlighting the role of iKGs in linking biological and clinical knowledge of individual patients. |  • Acknowledging challenges in data fragmentation, noncommensurability, and semantic inference within cardiovascular data.
Personalized Health Knowledge Graph [113] |  Aims to manage chronic diseases more effectively using IoT data analytics and explicit knowledge. |  • Proposes a methodology to build PHKG, integrating heterogeneous data sources. • Offers a solution for contextualizing and personalizing healthcare information. |  • The paper acknowledges the complexity in semantic integration of diverse data. • It highlights the challenges in tailoring generic knowledge to individual patients.
Developing an intuitive graph representation of knowledge for nonpharmacological treatment of psychotic symptoms in dementia [118] |  • Develop a knowledge graph for nonpharmacological treatment of psychotic symptoms in dementia. • Enhance understanding and management of dementia-related psychotic symptoms through nonpharmacological methods. |  • Creation of the Dementia-Related Psychotic Symptom Nonpharmacological Treatment Ontology (DRPSNPTO). • Improvement in visualization and computerization of gerontological knowledge. |  N/A
Learning a health knowledge graph from electronic medical records [28] |  Automatically learn a health KG from EMRs to link diseases and symptoms and improve clinical decision-support systems. |  • A methodology for deriving health KG from EMR using probabilistic models. • Demonstration that the noisy OR model significantly outperforms other tested models. |  • Inherent difficulties in interpreting EMR data, especially the presence of complex patient conditions. • The reliance on rudimentary concept extraction pipelines. • Limitations related to the automatic inference of causal relationships from observational data.
Applying personal knowledge graphs to health [4] |  The paper focuses on leveraging PHKGs to enhance healthcare decision-making by integrating personal health information with broader knowledge graphs. |  • Proposing a conceptual framework for PHKGs, highlighting how they can support personalized, knowledge-driven healthcare applications by leveraging data from EHRs, IoT devices, and other health-related data sources. |  • Collecting and storing personal health knowledge from heterogeneous sources. • Linking personal health knowledge to external KGs enhances the PHKG with broader contextual information. • Maintaining the PHKG to ensure it remains up-to-date and accurate.

###  V-C Enhancing Clinical Trials

KGs represent a paradigm shift in clinical trail patient selection by offering
a structured, interconnected data framework that can encapsulate complex
patient information, medical histories, and potential trial criteria. In
clinical trials, patient-centric approaches are crucial because personalized
medicine tailors treatments to each patient’s characteristics, necessitating a
comprehensive understanding of patient data.

PCKGs are created and applied using diverse methodologies. Gortzis and
Nikiforidis [119] described an N-tier system that combines KGs with human
collaboration and scalable knowledge engineering tactics. In order to select
patients effectively, expert input must be combined with scalable data
structures. Xiang et al. [120] highlighted the standardization and structural
integration provided by KGs, which are essential for auxiliary diagnosis
systems in clinical trials. Strategies for developing KGs for patient
selection in clinical trials include the linkage of multimodal data types for
automatic diagnosis [99]. Nicholson and Greene [121] discussed machine
learning methods for constructing low-dimensional representations of KGs,
which support applications in genomic, pharmaceutical, and clinical domains.

Various studies have indicated significant improvements in the accuracy,
efficiency, and outcomes of clinical trial patient selection. For instance,
the Safe Medicine Recommendation (SMR) framework by Wang et al. [122] bridges
electronic medical records with medical KGs to learn patient-disease-drug
embeddings, enhancing the precision of clinical trial patient selection.
Compared with traditional ways of selecting patients for clinical trails,
PCKGs offer a more thorough and subtle approach. Several studies have
demonstrated the effectiveness of KGs in improving clinical trial design and
outcomes by providing a more holistic view of patient data, facilitating
personalized trial matching, and providing a more holistic view of patient
data [123] [124] [93]. Utlimately, the integration of patient-centric KGs in
the selection of clinical trial participants has the potential to transform
the field by improving the precision and personalization of patient care. As a
result of a better understanding of patient data, future trials will likely be
more adaptive, patients will be more engaged, and outcomes may be improved. In
Table IV we summarize a list of selected treatment decision applications in
terms of their impact and limitations in the field of PCKGs.

TABLE IV: A summary of selected literature on using PCKGs for “Enhancing Clinical Trials” Paper Title |  Focus/Objective |  Contribution(s) |  Limitation(s)
---|---|---|---
Recommending Individualized Interventions
Knowledge graph-based clinical decision support system reasoning [120] |  The focus of the paper is to highlight the benefits of using knowledge graphs over traditional hand-crafted rule databases in CDSSs |  The introduction of the Path-Ranking Algorithm (PRA) as a method for automatically discovering symptoms without human intervention. |  • The probability of certain paths in the KG may not be accurate. • The lack of details on the classification model used.
Constructing knowledge graphs and their biomedical applications [121] |  • Examining the construction and application of biomedical knowledge graphs. • Emphasizing how machine learning is transforming these processes. |  • Discussion of knowledge graph construction, including manual curation and text mining. • Review of representational learning techniques and their applications in biomedical fields. |  • Need for advanced techniques to handle complex sentence structures. • Limitations in current methods to represent diverse relationships in KGs. • Scalability and memory limitations in matrix factorization techniques.
SMR: Medical knowledge graph embedding for safe medicine recommendation [122] |  Developing a framework to recommend safe medicines by leveraging a heterogeneous graph that integrates patient data, diseases, and medicines. |  • Development of graph-based embedding models enabling the recommendation of newly emerged medicines effectively. • A novel method to recommend safe medicines for new patients and minimizing potential adverse drug reactions. • Introduction of the SMR framework as a new approach to the link prediction problem. |  • Dealing with the challenge of recommending safe medicines, especially new ones, to patients. • Minimizing potential adverse drug reactions in medicine recommendations is critical to patient safety.
Patient centric approach for clinical trials: Current trend and new opportunities[123] |  Exploring the shifting paradigm in clinical trials towards a more patient-centric model. |  Identifying new opportunities for the clinical research industry to adopt patient-centric approaches to accelerate drug development and improve trial outcomes. |  • The complexity and rising costs of clinical research. • Ensuring data transparency and building trust with patients participating in clinical trials.
Automatic diagnosis with efficient medical case searching based on evolving graphs[99] |  Developing a method for automatic diagnosis by improving medical case searching using evolving graphs, which dynamically incorporate new medical cases and knowledge. |  • Introduction of an evolving graph framework that integrates new medical cases and knowledge. • A novel method for medical case searching that leverages the evolving graph structure. • An optimization strategy for embedding learning in the heterogeneous graph. |  • Handling the dynamic nature of medical knowledge and cases. • Balancing the computational complexity of embedding learning in a continuously evolving graph structure. • Scalability and maintaining high accuracy and efficiency as the graph expands.

Although PCKGs have proven their value in areas such as recommending
individual interventions, predicting disease before onset, and improving
clinical trials, their utility goes far beyond these. It is increasingly
becoming common to use KGs in other innovative healthcare applications, such
as optimizing hospital workflows, tailoring patient engagement strategies, and
even developing telemedicine platforms. With these innovative tools,
healthcare can be revolutionized by providing a more holistic, integrated view
of patient data, as well as new opportunities for research and treatment
methods.

Having explored the diverse applications and use cases of PCKGs in various
domains, we now turn our attention to the challenges and future directions in
this field. This transition allows us to critically examine the current
limitations and envision potential advancements that could further enhance the
utility and effectiveness of PCKGs.

##  VI Research Challenges & Discussion

PCKGs in healthcare are designed to provide a comprehensive, unified view of
patient data by integrating information from various sources, including EHRs,
medical literature, and patient-generated data. These graphs aim to support
better clinical decision-making and personalized patient care by representing
complex medical data in an interconnected format that is more accessible and
actionable for healthcare providers.

The current state of research in PCKGs is focused on overcoming several key
challenges to maximize their potential in healthcare. Ji et al. [78] discuss
the difficulties in knowledge acquisition, completion, and temporal KG
development, which are crucial for maintaining up-to-date and comprehensive
patient profiles. Building on this foundation, Chen et al. [25] highlight the
need for robustness in PCKGs, particularly in addressing sample size
limitations and unmeasured confounders, to extend models to larger patient
visits. Moreover, Rastogi and Zaki [125] emphasize the importance of
designing, building, and operationalizing PCKGs that are tailored to
individual patients. However, a significant hurdle remains in the actual
construction of PCKGs. As noted by Gyrard et al. [113] and Cong et al. [126]
point out the complexities in constructing PCKGs, which are often time-
consuming and heavily reliant on the quality of source data. A significant
challenge in the application of traditional KG embedding methods to patient-
centric healthcare applications is their struggle with structural sparsity. Hu
et al.[127] argue that conventional methods, such as TransE and ConvE, while
adept at mapping entities and relationships into a vector space, falter due to
their reliance solely on KG triplets, neglecting the rich auxiliary texts that
describe entities. This limitation significantly limits the comprehensiveness
and utility of KGs in capturing detailed patient information and medical
knowledge, indicating a key challenge in leveraging KGs for complex healthcare
applications.

Data quality and standardization remain significant challenges, as
heterogeneous data structures, poor data quality, and varying medical
standards complicate the integration of data into a coherent KG Zhang et
al.[128]. The integration of PCKGs into clinical workflows also presents
challenges, as it requires the development of systems that complement
healthcare providers’ routines without causing disruptions [119].
Additionally, scalability is another concern, as PKGs must be able to
incorporate an ever-increasing amount of data from diverse sources, including
genomic information and patient lifestyle data [99]. Patient data privacy is a
critical concern, particularly in the context of utilizing PCKGs, which
involve handling sensitive personal health information and require strict
privacy controls to safeguard patient confidentiality. The use of patients’
data for various purposes, such as consultations, research, and emergency,
poses a significant challenge for authorization systems, emphasizing the need
for robust privacy protection [129][89]. Furthermore, real-time data analysis
within PCKGs is technologically demanding, requiring advanced computational
methods to process and analyze data promptly for it to be clinically relevant
[130].

In light of these challenges, it becomes clear that continued technological
and methodological innovations are necessary to enhance the predictive
capabilities of PCKGs. The use of advanced analytics, machine learning, and
semantic web technologies could be key. Additionally, as PCKGs become more
integrated into healthcare delivery, it becomes increasingly important to
address regulatory and ethical considerations. This requires collaboration
among computer scientists, healthcare professionals, and policymakers to align
the development of PCKGs with broader healthcare objectives.

##  VII Conclusion & Future Directions

This literature review of PCKGs explores their development, evaluation,
processing techniques, applications, challenges, and prospects. PCKGs
represent a field in healthcare informatics that aims to revolutionize
personalized patient care by integrating and synthesizing diverse healthcare
data sources. The review highlights the current state-of-the-art methodologies
for constructing and evaluating PCKGs, emphasizing the importance of
qualitative and quantitative approaches to assess their effectiveness in
healthcare settings. In addition to construction and evaluation, the review
delves into innovative processing techniques such as reasoning, semantic
search, and inference. These techniques significantly enhance the accuracy and
efficiency of PCKGs, ultimately improving patient-centered care. Furthermore,
exploring different applications of PCKGs in healthcare—including disease
prediction, personalized treatment recommendations, and advancements in
clinical trials—reveals their potential to transform healthcare through
personalized and predictive medicine.

Future directions in PCKG research include leveraging advanced analytics and
machine learning to improve predictive capabilities, which could lead to more
accurate and timely interventions [131]. In addition, semantic web
technologies are also predicted to play a significant role in enhancing the
accessibility and utility of PCKGs [93]. Building on this momentum,
personalized medicine emerges as a promising area where PCKGs can make a
substantial impact. By linking genomic data with clinical outcomes, treatments
can be tailored to individual patients, offering a more personalized approach
to healthcare [132]. To further this advancement, methodological innovations,
such as new algorithms for data harmonization and user interface design, are
crucial. These innovations are needed to address current challenges and
facilitate the broader adoption of PCKGs in clinical practice [133].
Simultaneously, as PCKGs become more integrated into healthcare delivery,
regulatory and ethical considerations gain prominence. These aspects are
critical to ensuring that the deployment of PCKGs adheres to the highest
standards of patient care and data management. Therefore, cross-disciplinary
collaboration becomes essential for advancing PCKG technology. This involves
computer scientists, healthcare professionals, and policy-makers working
together to ensure that the development of PCKGs aligns with broader
healthcare objectives and respects ethical guidlines[134][135].

PCKGs represent a significant advancement in healthcare informatics. They can
transform patient outcomes by enabling early detection and personalized
treatment plans. Despite the challenges involved, the future outlook for PCKGs
is promising as they have the potential to improve patient care and healthcare
delivery significantly. Ongoing research efforts and interdisciplinary
collaboration will be crucial in fully realizing their novel impact on
healthcare.

## Acknowledgments

This work was supported by PATENT Lab (Predictive Analytics and TEchnology
iNTegration Laboratory) at the Department of Computer Science and Engineering,
Mississippi State University.

## Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any
commercial or financial relationships that could be construed as a potential
conflict of interest.

## Author Contributions

All authors contributed to producing the paper. All authors contributed to the
article and approved the submitted version.

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