# Artful Path to Healing: Using Machine Learning for Visual Art Recommendation
to Prevent and Reduce Post-Intensive Care Syndrome (PICS)

Bereket A. Yilma  University of LuxembourgLuxembourg ,  Chan Mi Kim
University of TwenteThe Netherlands ,  Gerald C. Cupchik  University of
TorontoCanada and  Luis A. Leiva  University of LuxembourgLuxembourg

(2024)

###### Abstract.

Staying in the intensive care unit (ICU) is often traumatic, leading to post-
intensive care syndrome (PICS), which encompasses physical, psychological, and
cognitive impairments. Currently, there are limited interventions available
for PICS. Studies indicate that exposure to visual art may help address the
psychological aspects of PICS and be more effective if it is personalized. We
develop Machine Learning-based Visual Art Recommendation Systems (VA RecSys)
to enable personalized therapeutic visual art experiences for post-ICU
patients. We investigate four state-of-the-art VA RecSys engines, evaluating
the relevance of their recommendations for therapeutic purposes compared to
expert-curated recommendations. We conduct an expert pilot test and a large-
scale user study (n=150) to assess the appropriateness and effectiveness of
these recommendations. Our results suggest all recommendations enhance
temporal affective states. Visual and multimodal VA RecSys engines compare
favourably with expert-curated recommendations, indicating their potential to
support the delivery of personalized art therapy for PICS prevention and
treatment.

Recommendation; Personalization; Artwork; User Experience; Machine Learning;
intensive care unit; rehabilitation; Health

††journalyear: 2024††copyright: rightsretained††conference: Proceedings of the
CHI Conference on Human Factors in Computing Systems; May 11–16, 2024;
Honolulu, HI, USA††booktitle: Proceedings of the CHI Conference on Human
Factors in Computing Systems (CHI ’24), May 11–16, 2024, Honolulu, HI,
USA††doi: 10.1145/3613904.3642636††isbn: 979-8-4007-0330-0/24/05††ccs:
Information systems Personalization††ccs: Information systems Recommender
systems††ccs: Computing methodologies Learning latent representations††ccs:
Applied computing Media arts Figure 1. Overview of our art therapy approach
for PICS prevention and treatment.

##  1\. Introduction

Patients in the intensive care unit (ICU) generally undergo stressful and
traumatic experiences stemming from critical illness, medical procedures,
pain, and a hostile environment (Egerod et al., 2015). Even after ICU
discharge, these patients are vulnerable and at a high risk of readmission to
the hospital and the ICU (van Beusekom et al., 2019; Shankar-Hari et al.,
2020). Post-ICU patients often suffer from post-intensive care syndrome (PICS)
which refers to “new or worsening impairments in physical, cognitive, or
mental health arising after critical illness and persisting beyond acute care
hospitalization” (Needham et al., 2012). PICS is quite common, affecting up to
75% of patients discharged from the ICU (Needham et al., 2013; Rawal et al.,
2017; Pandharipande et al., 2013; Davydow et al., 2013) and reduced quality of
life and hindrances in reintegrating into society of post-ICU patients (Dowdy
et al., 2005; Griffiths et al., 2013). Psychological aspects of PICS include
depression, anxiety disorders, and post-traumatic stress disorder (PTSD)
(Inoue et al., 2019). While there is growing interest in their prevention and
treatment, there exist limited interventions available, such as ICU follow-up
clinics (Cuthbertson et al., 2009; Inoue et al., 2019) and ICU diaries (Blair
et al., 2017; Inoue et al., 2019), and there is a need for more diverse and
effective approaches.

Visual art has been widely utilized to promote psychological well-being in
clinical environments. While art therapy is commonly understood as a form
involving creative activities, in this paper, we use ’art therapy’ as an
umbrella term where art serves as a medium for therapeutic benefits (Dalley,
2008; Geue et al., 2010). This includes various forms, such as engaging with
existing artwork to stimulate emotions and self-reflections. Art therapy, for
example, has been employed as a method to address various forms of
psychological disorders, including depression (Bar-Sela et al., 2007; Geue et
al., 2010), anxiety (Geue et al., 2010), and PTSD (Spiegel et al., 2006). This
approach leverages the unique characteristics of visual art, such as diverse
styles that activate interpretation and imagination, and the ability to
stimulate the expression of memories and specific emotions (Spiegel et al.,
2006), which have demonstrated effectiveness in addressing these psychological
disorders (Bar-Sela et al., 2007; Geue et al., 2010; Spiegel et al., 2006). In
the context of hospitals in general, as well as critical care settings such as
the ICU, the use of visual art as a positive distraction has also demonstrated
its effectiveness in reducing stress, anxiety, and pain perception (Ulrich and
Gilpin, 2003a; Nanda et al., 2008). This body of evidence showcases the
potential of visual art as an intervention for addressing the psychological
aspects of PICS.

Previous studies have suggested the importance of personalization in providing
positive distractions to enhance effectiveness (Sharda et al., 2019) while
minimizing potential side effects (Newbold et al., 2017). This indicates that
for visual art to serve as a positive distraction, it is crucial that it
resonates with the patient’s emotional needs, emphasizing the significance of
selecting appropriate art for each patient. Furthermore, to achieve a
prolonged effect of positive distraction, a continuous supply of personalized
art is necessary, which entails a large number of artworks.

In light of these challenges, it becomes evident that embracing a personalised
methodology for facilitating the selection of paintings in art therapy is not
merely advantageous, but of paramount importance. The intersection of
personalised medicine and art therapy holds the potential to revolutionise the
landscape of PICS treatment. Particularly recent advances in Machine Learning-
based Visual Art Recommendation Systems (VA RecSys) hold great potential to
open a novel avenue for tackling the challenges of artwork selection in a
personalised and nuanced manner to be used for art therapy of post-ICU
patients and beyond. By integrating these systems, we can bridge the gap
between the vast universe of artworks and the unique emotional needs of each
patient, thereby supporting experts in the selection process of curated
artworks that resonate with the penitent’s distinct cognitive and emotional
requirements.

In this paper, we set out to explore the potential benefits of integrating
Machine Learning (ML) based VA RecSys within the framework of PICS treatment
using art therapy. To the best of our knowledge, there are no prior works
leveraging VA RecSys in a therapeutic context. Therefore, we formulate the
following research question: Can VA RecSys algorithms support the
psychological well-being of post-ICU patients through personalized art
therapy?

In pursuit of this investigation, we propose approaches to integrate state-of-
the-art VA RecSys engines within the context of PICS prevention and reduction,
evaluating their potential efficacy and relevance. We explore four VA RecSys
engines that have shown superiority in uncovering complex semantic
relationships of artwork and have been successfully applied for personalised
recommendation tasks (Yilma and Leiva, 2023a, b). In particular, we trained
three uni-modal engines on image and textual data of artworks, and one
multimodal engine that fuses both image and text. For our image-based approach
we use the popular Residual Neural Network (ResNet) (He et al., 2016b), for
our text-based approach we adopt both Latent Dirichlet Allocation (LDA) (Blei
et al., 2003) and Bidirectional Encoder Representations from Transformers
(BERT) (Devlin et al., 2018), whereas for our fusion approach we use
Bootstrapping Language-Image Pre-training (BLIP) (Li et al., 2022). The
learned representations are used to derive personalised artwork
recommendations for therapy that are presumably matching penitents’ emotional
needs.

In sum, this paper makes the following contributions:

  * •

We develop and study four advanced VA RecSys engines using different backbone
architectures (ResNet, LDA, BERT, and BLIP) to support PICS prevention and
reduction through guided art therapy.

  * •

We conduct a usability test with 4 healthcare experts to assess the
appropriateness of VA RecSys engines and a large-scale study with 150 post-ICU
patients to assess the efficacy of the proposed VA RecSys engines, as compared
to expert-curated recommendations.

  * •

We contextualise our findings and provide guidance about potential strategies
to integrate ML-based VA RecSys in a personalised PICS intervention and
beyond.

##  2\. Related work

###  2.1. Approaches to prevent and reduce PICS

There are several interventions focused on preventing PICS. The ABCDEF bundle
(Pun et al., 2019) is a commonly used strategy for preventing PICS which
provides practical ways to promote patients’ state of being awake, autonomy,
and rehabilitation. The bundle has demonstrated its effectiveness in improving
the likelihood of survival, mitigating delirium, and reducing physical
restraints in ICU patients (Pun et al., 2019).

Environment management is another prevention strategy focusing on addressing
environmental factors that are hostile to ICU patients which are associated
with delirium and sleep deprivation, leading to PICS (Inoue et al., 2019).
Environment management includes the reduction of negative stimuli, such as
overloaded background noise and lighting, which have been shown to improve
sleep quality (Demoule et al., 2017)(Litton et al., 2016) and reduce delirium
among ICU patients (van de Pol et al., 2017). The provision of positive
stimuli is another way of environment management. Exposure to nature view, for
example, demonstrated to shorten the length of hospital stay and reduce
complications among patients (Ulrich, 1984). In addition, music intervention
reduces anxiety and stress-related measures in ICU patients (Lee et al.,
2017).

ICU diary is also a popularly practiced intervention supporting patients to
understand what has happened during their ICU stay including times during
sedation, which is effective in reducing anxiety, depression, and PTSD
symptoms (Inoue et al., 2019). Nevertheless, implementing all the
aforementioned interventions requires systemic changes. Furthermore, due to
their non-pharmacological nature, these prevention strategies often confront
constraints related to increased workload for healthcare professionals (Kotfis
et al., 2020; Ramnarain et al., 2021).

Next to preventing PICS, it is also important to recognize early symptoms and
provide interventions to avoid further development into, for example, acute
stress disorder (ASD) and post-traumatic stress disorder (PTSD). ICU follow-up
care is one of the most practiced interventions that address the psychological
aspects of PICS (Ramnarain et al., 2021)(Inoue et al., 2019). ICU follow-up
care aims to support patients through their transition period from hospital to
home with guidance. This approach has gained significant traction in both
Europe and North America in recent years and found to mitigate symptoms of
PTSD stemming from ICU stay (Jensen et al., 2015). However, the current ICU
follow-up care lacks a standardized structure, resulting in variations in
interventions between hospitals, which makes it difficult to track the
effectiveness of these interventions (Jensen et al., 2015). On the other hand,
this variability paves the way for the advancement and implementation of
individualized interventions, which often adopt technology. Overall, these
interventions include guided meditation in natural settings using VR which
could reduce pain and stress (Pourmand et al., 2018), and gamified cognitive
behavioral therapies for reducing depression (Li et al., 2014). Furthermore,
there exist various strategies to apply technology for ICU delirium prevention
(Kim et al., 2021), many of which can be readily adapted to a post-intensive
care environment. As such, there is the potential to enhance the
rehabilitation of ICU patients through ICU follow-up care with a personalized
methodology enabled by technology.

###  2.2. Therapeutic visual experience and personalisation

Since the study by Ulrich (1984), which demonstrated the impact of having
access to a nature view on enhancing the health outcomes of ICU patients,
exposure to nature, and particularly its visual experiences, has gained
interest as a nonpharmacological intervention (Hamilton and Shepley, 2010).
Interventions are introduced wherein visual nature experiences play a role as
a positive distraction (Veling et al., 2021; Waszynski et al., 2018),
generating positive feelings and maintaining attention for patients without
inducing stress.

Next to the exposure to nature, visual art is another popularly used positive
distraction in hospital settings. A growing body of evidence suggested the
impact of appropriate art on the health outcomes of patients (Hathorn and
Nanda, 2008). Research also found that visual art with nature content is
effective in reducing stress, anxiety, and perceived pain in critically ill
patients (Ulrich and Gilpin, 2003b; Nanda et al., 2008). Importantly, the
characteristics of visual art, including subject matter and style, have been
found to strongly influence its impact on patients (Ulrich and Gilpin, 2003b;
Hamilton and Shepley, 2010). Representative pictures dominated by nature, such
as landscapes with trees and water, have been shown to be more effective in
reducing anxiety and pain than other types, such as art without nature content
(Ulrich et al., 1993). Notably, the study showed that abstract art with
rectilinear and straight-edge forms brings strong negative reactions (Ulrich
and Gilpin, 2003b).

The therapeutic effects of visual nature experiences are explained by
evolutionary theory (Appleton, 1975) and the Biophilia hypothesis (Wilson,
1986). These theories (Wilson, 1986; Appleton, 1975) propose that over
millions of years of evolution, humans have become genetically predisposed to
respond positively to natural settings that promoted well-being and survival
for early humans. Negative effects of certain visual arts, on the other hand,
can be explained by the Emotional Congruence theory (Ulrich and Gilpin,
2003b), which suggests that our perception of stimuli is influenced by our
emotional states. This implies that abstract art, being open to
interpretation, can lead patients in highly stressful situations to project
negative emotions onto their interpretation. Consequently, they end up
experiencing adverse visual experiences, as was demonstrated in Ulrich’s study
(Ulrich and Gilpin, 2003b), where immediate removal of such pictures became
necessary. These instances underscore that while the use of art in a
therapeutic setting is compelling, the application of art requires careful
customization and personalization to ensure effectiveness. Recognizing these
needs, Hathron (Hathorn and Nanda, 2008) emphasized the importance of
considering individualized needs when selecting art for healing.

Art has been utilized outside of hospital settings, such as in the treatment
of PTSD. Art therapy in these contexts takes various forms, ranging from
exposure to creative techniques, with debatable effects (Schouten et al.,
2015). In this study, we focus on the aspects of visual arts as a positive
distraction. Taking this approach, we aim to improve temporary stress and
evoke positive emotions among post-ICU patients, both during their time in the
post-ICU clinic and throughout the rehabilitation stage. To enhance the
positive impact, we incorporate techniques from narrative therapy (Madigan,
2011) which encourages patients to explore deeper into the meanings of the
visual arts and engage in prolonged exposure to the therapeutic elements of
the art. We will further elaborate our approach in the method section.

###  2.3. Visual art recommendation systems

VA RecSys represent an emerging field at the intersection of technology, art,
and user preferences. These systems harness data-driven methodologies,
particularly powered by machine learning algorithms, to facilitate
personalised art recommendations, enabling users to discover artworks that
resonate with their personal aesthetic inclinations. VA specifically paintings
are both high dimensional and semantically complex, leading to a diverse and
subjective set of emotional and cognitive reflections on users (Wu et al.,
2020). Paintings reflect the complex and intricate interplay of concepts
ranging from individual ideas and beliefs to concepts with cultural and
historical significance of a society (Palumbo et al., 2023).

With the proliferation of artworks in recent years, coupled with an increasing
demand for personalization, VA RecSys has gained traction across diverse
domains spanning from online platforms, to cultural heritage institutions
aiming to enhance visitors experience and engagement. Studies like (Esman,
2012; Helal et al., 2013) have shown a number of advantages and highlighted
the significant role of VA RecSys. As the study of Falk et al. (Falk, 2016)
discuses, the main motivation of museum visitors is to have fun, experience
art, learn new things, feel inspired, and interact with others. Thus, using VA
RecSys empowered digital museum guides, visitors’ expectations are not only to
be exposed to artwork that matches their interest but also learn more and have
access to more information (Helal et al., 2013). Achieving this fundamentally
requires VA RecSys to uncover complex semantics and abstract relationships
between art works to derive recommendations. The earliest works such as Kuflik
et al. (Kuflik et al., 2014) and Deladiennee et al. (Deladiennee and Naudet,
2017) proposed a graph-based semantic VA RecSys that relies on an ontological
formalisation of knowledge.

In recent years with the advancements in Artificial intelligence (AI)
particularly Neural Networks (NN) demonstrating enormous success in capturing
latent semantic structures and relationships from data, the RecSys domain
started to adopt representation learning techniques (He et al., 2016a).
Following this, a number of VA RecSys works emerged by learning latent
representations from images of artworks (Messina et al., 2017, 2019, 2020) and
from textual descriptions of paintings (Yilma et al., 2021, 2020) as well as
from their combinations (Yilma and Leiva, 2023b, a) demonstrating the power of
NNs to derive meaningful and personalised recommendations. However, the
integration of VA RecSys in healthcare and specifically within the context of
PICS rehabilitation remains an unexplored domain. This paper aims to bridge
this gap by investigating the potential of VA RecSys in aiding the art therapy
process for PICS patients. By comparing VA RecSys-generated recommendations
with expert-curated ones, this study seeks to enrich the current understanding
of the role of VA RecSys in personalised healthcare interventions.

##  3\. Background: Learning latent representations of visual art

Representation learning is a powerful computational concept that involves
automatically uncovering the underlying structure within complex data (Bengio
et al., 2013). It is a process where an algorithm learns to convert raw data
inputs into more compact, meaningful, and feature-rich representations. These
representations capture essential patterns, relationships, and characteristics
within the data, enabling more effective analysis, understanding, and
utilization (Zhong et al., 2016).

In the context of VA RecSys, representation learning plays a pivotal role in
converting intricate visual elements into condensed yet informative forms.
This process involves training algorithms on text and/or image modalities,
often based on neural networks, to recognise and extract not only distinctive
features present in paintings, such as colours, shapes, and textures but also
complex concepts embodied within artworks such as the emotional and cognitive
reflections they trigger which are not always observable to the naked eye
(Yilma and Leiva, 2023a). The learned representations encode these features in
a way that captures the essence of the artwork’s semantics as well as visual
identity. This goes beyond mere pixel values, as the algorithms internalise
the higher-level characteristics that make each piece of art unique (Yilma and
Leiva, 2023b).

Based on the input data source there are two notable paths in representation
learning literature which are unimodal and multimodal approaches (Cooney et
al., 2022). As discussed in Section 2.3, we draw inspiration from the recent
successes of VA RecSys employing NN-based representation learning, and
observing how the resulting personalised recommendations capture hidden
semantics and benefit users in many ways such as learning, discovery, enhanced
engagement and better interaction experience. The key idea of representation
learning in this setting is that textual and visual modalities of paintings
are used to learn an embedding space where similar items are represented close
to each other in the embedding space as explained in the following
subsections. Figure 2 and 3 summarise the painting representation learning
approaches we propose and study for PICS rehabilitation therapy.

###  3.1. Unimodal VA representation learning

This approach extracts and encodes inherent features of paintings from a
single type of data modality (i.e., image or textual description).

####  3.1.1. Image-based VA representation learning

Today, image feature extraction techniques predominantly rely on pre-trained
Convolutional Neural Network (CNN) architectures, such as AlexNet (Krizhevsky
et al., 2017), GoogLeNet (Szegedy et al., 2015), and VGG (Simonyan and
Zisserman, 2014). An exemplar of this trend is the winner of the 2015 ImageNet
challenge, ResNet, introduced by He et al.(He et al., 2016b). ResNet pioneered
the integration of residual layers to facilitate the training of very deep
CNNs, setting the record with architectures comprising over 100 layers. A
prominent version of this architecture is ResNet-50, featuring 50 layers,
trained extensively on a vast repository of images from the ImageNet
database.111https://www.image-net.org Consequently, ResNet-50 has assimilated
intricate feature representations across a diverse spectrum of images and has
showcased its potential as a superior visual feature extractor compared to
other pre-trained models(Ikechukwu et al., 2021; Li and Lima, 2021; Barata et
al., 2018).

To extract latent visual features (image embeddings) from paintings, we
employed the ResNet-50 model pre-trained on the ImageNet dataset. By
channelling each painting image through the network, we derived a
convolutional feature map, resulting in a feature vector representation. Upon
completing the extraction process for all image features within the dataset
containing $m$ number of images, we produce a matrix
$\mathbf{A}\in\mathbb{R}^{m\times m}$, with each entry reflecting the cosine
similarity measure among all image embeddings. Cosine similarity is an
effective metric to find item similarities from embedding spaces, which is
commonly used in data mining and information retrieval (Nguyen and Amer, 2019;
Yilma and Leiva, 2023b). This matrix encapsulates the latent visual
distribution across all images, serving as a foundation for calculating
similarities among paintings for a VA RecSys task discussed in Section 4

####  3.1.2. Text-based VA representation learning

Learning latent representations of paintings from their textual descriptions
was proven a powerful technique to uncover hidden semantic concepts that are
embodied across artwork (Vlachidis et al., 2017; Yilma et al., 2020; Yilma and
Leiva, 2023a). In this work, we adopt two of the popular text-based
representation learning approaches that demonstrated success in VA RecSys
tasks namely, Latent Dirichlet Allocation (LDA) and Bidirectional Encoder
Representations from Transformers (BERT).

##### LDA

Our first text-based VA RecSys approach is LDA, an unsupervised generative
probabilistic model proposed by Blei et al. (Blei et al., 2003). LDA attempts
to model a collection of observations as a composite of distinct categories,
or topics. In this context, each observation corresponds to a document, and
the features are the presence, occurrence, or count of words, while the
categories constitute the underlying topics. Notably, the specifics of the
topics are not predefined; only the number of topics is chosen beforehand.
These topics are learned as probability distributions over the words within
each document.

The procedure for constructing an LDA model within the VA RecSys framework is
as follows. We begin by curating a collection of documents, each containing
textual information about individual paintings. Subsequently, a desired number
of topics, denoted as $k$, is determined, and each word $w$ within the
document collection is assigned to a topic. This assignment is guided by
$\theta_{i}\sim Dir(\alpha)$, where $\theta$ signifies the topic distribution
for a document $d$, $\alpha$ represents the per-document topic distribution,
$i\in{1,...,k}$, and $Dir(\alpha)$ denotes a Dirichlet distribution spanning
the $k$ topics. The learning phase involves computing conditional
probabilities $P(t|d)$ (representing the likelihood of topic $t$ given
document $d$) and $P(w|t)$ (indicating the likelihood of word $w$ given topic
$t$). A comprehensive discourse on LDA topic modeling is presented in (Blei et
al., 2003) and (Jelodar et al., 2019). Upon completing the training of the LDA
model over the entire textual dataset containing $m$ number of documents
representing each painting, a matrix $\mathbf{A}\in\mathbb{R}^{m\times m}$ is
generated. Each entry $a(i,j)$ within this matrix corresponds to the cosine
similarity measure between document embeddings. This matrix encapsulates the
latent distribution of topics across all documents, which is utilised for
calculating semantic similarities among paintings to derive recommendations.

##### BERT

Similarly, for the second approach with BERT, we start by curating documents
for each painting. Then the feature learning process goes through three
distinct phases. Firstly, we transform each painting document into an
embedding representation by leveraging the pre-trained SBERT large language
model.222In our implementation, we employed the all-MiniLM-L6-v2 version to
optimize performance, though alternative versions can also yield suitable
painting embeddings. This transformation maps sentences and paragraphs into a
384-dimensional dense vector space (Reimers and Gurevych, 2019). Secondly, we
employ the uniform manifold approximation and projection (UMAP) algorithm
(McInnes et al., 2018) to reduce the dimensionality of these embeddings. UMAP,
a dimension reduction technique, facilitates the transformation of multi-
dimensional data points into a two-dimensional space. This step enhances
efficiency while preserving the original embeddings’ overarching structure.
Thirdly, we leverage the HDBSCAN algorithm (Campello et al., 2013), a soft-
clustering technique, to semantically cluster the reduced embeddings. HDBSCAN
avoids the misallocation of unrelated documents to clusters, thus enhancing
the quality of clustering outcomes.

From these clusters, we extract latent topic representations using a custom
class-based term frequency-inverse document frequency (c-TF-IDF) algorithm.
This algorithm generates importance scores for words within a topic cluster.
The essence of c-TF-IDF lies in its capacity to provide topic descriptions by
identifying the most vital words within a cluster. Words boasting high c-TF-
IDF scores are selected for each topic, thereby creating topic-word
distributions for every document cluster. A more detailed discussion of our
topic modeling strategy with BERT can be found in the work of Grootendorst et
al. (Grootendorst, 2022). Similar to the LDA approach, upon the completion of
training the BERT model across the entire textual dataset of size $m$, we
produce a matrix $\mathbf{A}\in\mathbb{R}^{m\times m}$. Each entry within this
matrix quantifies the cosine similarity measure between all document
embeddings. As with the LDA approach mentioned above, this similarity matrix
captures the latent distribution of topics throughout all documents. Thus, it
can be utilised to compute similarities of paintings for a recommendation
task.

Figure 2. Overview of our unimodal approaches to learning latent
representations of paintings.

###  3.2. Multimodal VA representation learning

This approach combines information from multiple data sources, like images and
associated textual descriptions to create a unified representation space
(Ramachandram and Taylor, 2017). This joint embedding enables the exploration
of the interconnectedness between the inherent attributes of each modality.
The latent features extracted from images and textual descriptions are mapped
into the same embedding space, ensuring that semantically similar images and
corresponding textual descriptions are brought closer together (Li et al.,
2021). This synergy between textual narratives and visual aesthetics enhances
the potential for various applications in interpreting artworks. Among the
different approaches in the literature, we use Bootstrapping Language-Image
Pre-training (BLIP) (Li et al., 2022), which has demonstrated superior
performance in various downstream tasks, including VA RecSys.

BLIP is a technique that trains neural networks by combining language and
image data. It trains a model to predict either an image or text given the
other, in order to improve the model’s understanding of multimodal
relationships. BLIP uses a unified encoder-decoder model that can operate in
three modes. The first mode, the unimodal encoder, encodes image and text
separately. The second mode, the image-grounded text encoder, uses cross-
attention to inject visual information into the text encoder. The third mode,
the image-grounded text decoder, replaces bi-directional self-attention layers
with causal self-attention layers. During pre-training BLIP optimizes three
objectives: Image-Text Contrastive Loss (ITC), Image-Text Matching Loss (ITM),
and Language Modeling Loss (LM). ITC aligns the visual and text transformers
by encouraging similar representations for positive image-text pairs and
dissimilar representations for negative pairs. ITM classifies whether image-
text pairs are positive or negative. LM generates textual descriptions based
on images.

For our VA representation learning task, we utilized the pre-trained BLIP
model as a multimodal feature extractor. First, we extract multimodal features
and use the ITM head to compute ITM scores for each painting, generating
probability-matching scores for each image-text pair. Then, we compute a
matrix $\mathbf{A}\in\mathbb{R}^{m\times m}$ where each entry
$\mathbf{A}_{ij}$ is the probability matching score between the joint painting
embeddings which can be used to compute similarities for a VA RecSys tasks.
See Figure 3 for an illustration of our multimodal approach to learning latent
semantic representations of paintings with BLIP.

Figure 3. Overview of our multimodal approach with BLIP to learning latent
semantic representations of paintings.

##  4\. Method: Personalised visual art recommendation for PICS therapy

To enhance the potential therapeutic benefits of a visual art experience, we
designed a personalized guided art therapy. To tap into participants’ latent
needs and preferences, the process starts with inviting them to choose their
preferred painting from sample paintings. Based on their selections,
participants were subsequently presented with a set of three paintings,
carefully chosen to align with their preferred sample painting. This inclusion
of multiple paintings was intended to extend the duration of exposure to the
painting. Additionally, for each of these selected paintings, we provided
accompanying text guidance to facilitate active engagement and thoughtful
reflection among participants. In this study, we explore two different
approaches of personalised VA recommendation strategies for PICS art therapy;
Expert-based and VA RecSys-based recommendation.

###  4.1. Expert recommendations

This established approach involves the curation of recommendations by
experienced clinicians. Their expertise allows them to choose artworks that
elicit desired emotional and psychological responses, in line with PICS
rehabilitation therapy objectives. The recommendation procedure is primarily
guided by the patients’ preference among a list of alternative paintings
presented to them in order to identify the paintings that they resonate most
profoundly with their journey towards recovery. Following this clinicians will
select a set of recommended paintings that evoke similar emotions and moods
that align with the therapeutic goals of the recovery journey.

###  4.2. VA RecSys recommendations

We consider unimodal and multimodal representation learning techniques
discussed in Section 3 that can learn features from both textual and visual
modalities paintings. Particularly we study four models; LDA and BERT to learn
text-based representations, ResNet for image-based representations, and BLIP
for the fusion of the two modalities.

Let $P=\\{p_{1},p_{2},\dots,p_{m}\\}$ be a set of image paintings and
$\mathcal{P}=\\{\boldsymbol{p}_{1},\boldsymbol{p}_{2},\dots,\boldsymbol{p}_{m}\\}$
be the associated embeddings of each painting according to LDA, BERT, ResNet
or BLIP. Once the dataset embeddings (latent feature vectors) are learned
using either model (LDA, BERT, ResNet or BLIP) we compute the similarity
matrix for all the paintings $\mathbf{A}$. Next, the preference of a patient
user $u$ is modelled by computing a ranking score for the paintings in the
dataset according to their therapeutic relevance (i.e. similarity to the
painting $p_{j}$ a user indicated to support their recovery). Thus, the
predicted score $S^{u}(p_{i})$ the user would give to each painting in the
collection $P$ is calculated as:

(1) $S^{u}(p_{i})=d(\boldsymbol{p}_{i},\boldsymbol{p}_{j})$

where $d(\boldsymbol{p}_{i},\boldsymbol{p}_{j})$ is the cosine similarity
between embeddings of paintings $p_{i}$ and $p_{j}$ in the computed similarity
matrix. Once the scoring procedure is complete, the paintings are sorted and
the $r$ most similar paintings constitute a ranked recommendation list. Figure
4 summarises our VA RecSys-based recommendation pipeline.

Figure 4. Overview of our proposed ML-based VA RecSys pipeline. Sample
paintings to recall ICU experience (left), our ML-based VA RecSys engines
(Middle) and healing painting recommendations (right)

##  5\. Materials

###  5.1. Sample paintings for preference elicitation

To elicit user preferences, we offer sample paintings, allowing users to
provide their input by choosing one of them. These sample paintings were
derived from a pre-study approved by the Ethics Review Panel of the University
of Twente, conducted with a total of 186 former patients, including 10 former
ICU patients.

In a pre-study, an initial selection of 18 nature paintings associated with
positive emotions, such as relaxing, cheerful, and awe-inspiring, was made by
an academic expert in affective design from WikiArt.333https://www.wikiart.org
These paintings varied in style including different levels of abstraction.
Subsequently, this set of paintings was narrowed down to 6, each of which was
found to strongly evoke one or multiple positive emotions. This selection
process involved evaluation by three experts in affective psychology,
environmental psychology, and affective and healthcare design, each with over
15 years of experience in their respective fields.

Using these final six paintings, we conducted an online survey to ask
participants to choose the painting that would best support their recovery.
The results of the pre-study showed that Painting 2 was chosen by the majority
of participants (n=63), followed by Painting 1 (n=32) and Painting 3 (n=32).
These three paintings are shown in Figure 4, left column (labels as ‘sample
paintings’) form top to bottom. In the present study, we use these top three
most frequently selected paintings as sample paintings for preference
elicitation to derive personalised recommendations. These paintings encompass
diverse styles and are believed to convey emotions associated with calmness,
restoration, and cheerfulness, respectively, providing a comprehensive
representation of the healing experience.

###  5.2. Dataset for generating recommended paintings

We used a dataset combining the nature paintings from the pre-study with a
collection containing 2,368 paintings from the National Gallery,
London.444https://www.nationalgallery.org.uk/ provided through the CrossCult
Knowledge Base.555https://www.crosscult.lu/ Every painting image within the
dataset is accompanied by a complementary set of text-based metadata. This
dataset’s configuration renders it well-suited for evaluating the proposed
feature learning methodologies. A representative data point is illustrated in
Figure 5.

Figure 5. Sample painting and associated metadata from the dataset.

For acquiring textual features via LDA and BERT models, we performed pre-
processing of the painting metadata. This encompassed concatenating text
fields, excluding punctuation symbols and stop-words, transforming to
lowercase, and applying lemmatization. Conversely, for the acquisition of
visual features through the ResNet model, we utilized the authentic painting
images.666All paintings are available under a Creative Commons (CC) license.
This procedure involved extracting convolutional feature maps using the pre-
trained ResNet-50 model. For the multimodal feature learning with BLIP, both
pre-processed text and image data sources were jointly utilized, as elaborated
in Section 3.

###  5.3. Ensuring a safe and sensitive deployment: Expert evaluation of VA
RecSys engines

Before delving into our comprehensive study involving end-users, particularly
PICS patients, we conducted a pilot test with experts from diverse relevant
domains, as a proactive measure to ensure their safety and well-being. The
primary objective was to preempt any potential harm or inadvertent elicitation
of negative emotions that might arise from the recommendations generated by
the VA RecSys engines. The core purpose of this usability test was to
ascertain the suitability of VA RecSys engine-generated recommendations for
deployment without requiring human intervention.

####  5.3.1. Apparatus

We created an overview of sample paintings and their respective
recommendations generated by each of the engines. The recommendation engines
were anonymized. Participants were provided one set of recommendations from
each VA RecSys engine at a time.

####  5.3.2. Participants

Four experts well-versed in diverse domains were recruited, including ICU
nursing, healthcare design research, and affective design research.

####  5.3.3. Design

The experts were exposed to all recommendation pipelines (within-subjects)
design and were asked to assess the appropriateness of each of the top-3
recommended paintings from all pipelines for all samples.

####  5.3.4. Procedure

Participants were invited for a one-to-one interview to assess the VA RecSys
engines recommendation. There, they were informed about the purpose of the
study. Then participants were provided with individual recommendation
paintings per sample. For each recommended painting, they were asked to
respond to the question “To what extent do the recommended painting align with
the original painting in terms of the overall experience they evoke? This
experience can be visual, semantic, or a combination of both.” They responded
in a 1-5 scale (1: not at all, 5: very much) as reported in Table 1.
Furthermore, they were also asked to give their expert opinion on the
appropriateness of the recommended paintings for the purpose of therapy.

####  5.3.5. Results

By leveraging the expertise of recruited professionals, we meticulously
assessed the engines to identify those that align harmoniously with
therapeutic goals and emotional well-being. The insights garnered from this
test served as a foundation for selecting engines that could be employed with
a higher degree of confidence, thereby fostering an environment of safety and
sensitivity in our subsequent studies involving PICS patients.

From the experts’ evaluation, we observed that both text-based VA RecSys
engines were not suitable for deployment in a real-world application. As they
were found to be dark and evoking negative emotions. Some of the expert
reflections on these recommendations are depicted in Figure 6. The figure
shows some examples of the comments provided by the experts. As can be
appreciated, some of the recommended images were too dark which could elicit
fear or contain images of violence that could trigger traumatic experiences.
Following this pilot we have decided to proceed with only three recommendation
methods for our user study; Expert, Visual and Multimodal. Figure 7 shows
examples of the top-2 recommendations from each of the expert validated
approaches.

|  | Fusion BLIP | Image ResNet | Text BERT | Text LDA
---|---|---|---|---|---
Expert 1 (Affective Design, ICU research, +10) | Painting 1 | 2 | 3 | 1 | 1
Painting 2 | 3 | 2 | 3 | 1
Painting 3 | 2 | 3 | 2 | 1
Expert 2 (Affective Design, +10) | Painting 1 | 3 | 2 | 2 | 1
Painting 2 | 3 | 2 | 1 | 2
Painting 3 | 2 | 4 | 2 | 1
Expert 3 (Affective Design, +10) | Painting 1 | 2 | 4 | 1 | 1
Painting 2 | 1 | 1 | 2 | 4
Painting 3 | 2 | 3 | 1 | 1
Expert 4 (ICU Nurse, +10) | Painting 1 | 1 | 4 | 2 | 2
Painting 2 | 4 | 2 | 3 | 3
Painting 3 | 2 | 3 | 2 | 1
Total |  | 27 | 33 | 22 | 19
Table 1. In our pilot test with Experts, they were shown 3 paintings from each
of the VA RecSys engines for all sample paintings. Figure 6. Expert
reflections on the top three direct recommendations from our text-based VA
RecSys engines (BERT and LDA). Figure 7. Top-2 recommendations for each sample
painting from expert-validated engines.

##  6\. Evaluation: User study

The main goal of our evaluation was to understand the user’s perception
towards the quality of our studied VA recommendation strategies for PICS
rehabilitation therapy, and ultimately to assess their efficacy in supporting
the healing journey of PICS survivors. We conducted a large-scale user study,
to be described later, that was approved by the Ethics Review Panel of the
University of Twente.

###  6.1. Apparatus

We designed an online guided therapy survey using Google
Forms777https://www.google.com/forms/about/ that first elicited the
preferences of participants by providing them with a set of paintings to
choose from that resonates with their healing journey. Then participants were
taken through a guided therapy session by using three paintings that were
recommended based on the elicited preference choices.

Figure 8.  Screenshots of our online survey for guided therapy: preference
elicitation (left) and recommendations (right).

###  6.2. Participants

We recruited a representative pool of $N=150$ participants via the Prolific
crowdsourcing platform.888https://www.prolific.co/ We identified people who
had conditions in the past that may potentially lead to PICS, such as COVID-19
patients who were treated in a hospital, cancer survivors, or having other
surgical issues. For this study, we focused on post-COVID patients, as the
likelihood of having developed PICS recently is higher. The main screening
criteria was “I have been officially diagnosed with COVID-19 (tested by a
licensed medical professional), and was treated in a hospital.”. Furthermore,
we enforced other criteria for eligibility: being fluent in English, minimum
approval rate of 100% in previous crowdsourcing studies in the platform, and
being active on the platform in the last 90 days.

Our recruited participants (74 female, 76 male) were aged 32.7 years (SD=10.9)
and could complete the study only once. Most of them lived in UK (40
participants), USA (28), or South Africa (35). Most participants had been
through general ward or a medical/surgical unit (67), or had been in ICU (44)
or in an Emergency Room (37). For most participants, the duration of their
stay in a hospital was less than a week (71) or between 1 to 2 weeks (50).
Most participants declared to suffer from anxiety (130) and/or depression
(127), indicating the presence of psychological components related to PICS
symptoms. The study took a median time of 23 min to complete and participants
were paid an equivalent hourly wage of $12/h. We also administered the Patient
Health Questionnaire-4 (PHQ-4) (Löwe et al., 2010) to collect signs of
psychological symptoms related to PICS: anxiety and depression. Most
participants (86%) exhibited at least one symptom, with the majority (70%)
showing symptoms for both of these conditions.

###  6.3. Design

Following the expert evaluation of our developed VA RecSys engines discussed
in Section 5.3, we deployed two engines together with the expert
recommendations: visual (ResNet-based VA RecSys) and multimodal (BLIP-based VA
RecSys). Each participant was only exposed to the recommendations generated by
one of the three groups (between-subjects design) and then went through guided
art therapy. Each group comprised 50 participants.

###  6.4. Procedure

We first assessed baseline and post-test affective states using two different
measures: a Pick-A-Mood tool (Desmet et al., 2016) for moods and the short
version of the Positive and Negative Affect Schedule (PANAS) scale (Watson et
al., 1988) for emotions. In order to reduce the cognitive load on participants
and to be more efficient, we selected 10 items that are relevant to PICS (for
negative emotions) and patient well-being (for positive emotions).
Particularly we consider 5 positive and 5 negative items (instead of 10
positive and 10 negative items) together with a neutral item to assess the
affective state of participants before and after guided art therapy.

Guided art therapy involves asking questions that encourage participants to
engage with the paintings, such as “Imagine yourself entering the painting and
exploring it. How did you feel while spending time in this painting?”
Participants were prompted to reflect on their experience and describe it in
three to four sentences.

Participants rated the provided painting recommendations in a 5-point Likert
scale. Our dependent variables are widely accepted proxies of recommendation
quality (Pu et al., 2011):

Accuracy::

The paintings match my personal preferences and interests.

Diversity::

The paintings are diverse.

Novelty::

I discovered paintings I did not know before.

Serendipity::

I found surprisingly interesting paintings.

We also collected two dependent variables that inform to what extent the
recommended paintings contributed to a sense of immersion and engagement:

Immersion::

How much do the recommended paintings contribute to your sense of immersion,
making you feel deeply involved or absorbed in the artwork?

Engagement::

To what extent do the recommended paintings contribute to your feeling of
engagement, capturing your attention and generating a sense of involvement or
interest?

###  6.5. Results

####  6.5.1. Comparison of recommended groups: Expert, Visual, and Multimodal

We investigated whether there were differences between the three
recommendation groups (i.e. Expert, Visual, and Multimodal). We used a linear
mixed-effects (LME) model where each dependent variable is explained by each
recommendation group. Participants are considered random effects. An LME model
is appropriate here because the dependent variables are discrete and have a
natural order.

We fitted the LME models (one model per dependent variable) and computed the
estimated marginal means for specified factors. We then ran pairwise
comparisons (also known as _contrasts_) with Bonferroni-Holm correction to
guard against multiple comparisons.

##### Analysis of recommendation quality measures

Figure 9 shows the distributions of user ratings for the user-centric
dependent variables of recommendation quality. Differences between groups were
not statistically significant in any case, with small to moderate effect
sizes. The largest effect sizes were observed when comparing Expert and
Multimodal recommendations in terms of diversity ($r=0.177$) and serendipity
($r=0.119$).

(a) Immersion (b) Engagement (c) Accuracy

(d) Diversity (e) Novelty (f) Serendipity

Figure 9. Distribution (box plots) of user ratings for the user-centric
dependent variables of recommendation quality. Dots denote mean values.

##### Analysis of changes in mood

Figure 10 shows the mood changes before and after therapy, for the three
recommendation groups we have considered in our study. In all three groups, a
mood enhancement effect of guided art therapy was observed. When comparing it
to the baseline where the majority of participants were in a negative mood
(44.6%), after guided art therapy the majority of participants reported being
in a positive mood (70.5%), with only a minority remaining in a negative
(13.6%) or neutral (15.8%) mood.

Figure 10.  Mood improvement comparison before and after art therapy.

Figure 11 shows the change in scores after therapy, aggregated according to
the ten items of the PANAS scale. Differences between groups were not
statistically significant in any case, with small to moderate effect sizes.
According to an item-independent analysis, the largest effect sizes were
observed in terms of the ‘afraid’ item, when comparing Expert recommendations
against Visual ($r=0.181$) and Multimodal ($r=0.175$) recommendations,
followed by the ‘scared’ item when comparing Expert and Visual recommendations
($r=0.122$). Upon further examination, users in the Visual group did not
change their scores for the ‘afraid’ item (the median difference is 0). This
was also the case for users in both the Visual and Multimodal group with
regards to the ‘scared’ item.

Figure 11. Emotion score changes according to the Positive Affect Negative
Affect Schedule (PANAS) Scale.

####  6.5.2. Analysis of user reflections

We conducted sentiment analysis on the reflections of participants to gain
deeper insights into their experience. We used the pre-trained transformer-
based sentiment analysis model bert-large-uncased-sst2 from the Hugging Face
Transformers library.999https://huggingface.co/models This model is a fine-
tuned version of bert-large-uncased which was trained on the Stanford
Sentiment Treebank v2 (SST2)101010https://nlp.stanford.edu/sentiment; part of
the General Language Understanding Evaluation (GLUE)
benchmark111111https://gluebenchmark.com. It is well-suited for a wide range
of NLP tasks due to its large size and general language understanding
capabilities. It has also demonstrated exceptional success in sentiment
analysis tasks. Leveraging this model the result of our sentiment analysis,
illustrated in Figure 12 indicates overwhelmingly positive sentiments
expressed by participants in response to their interaction with the
recommended paintings. However, it is noteworthy that a subtle trend emerged
within the expert group, where a marginal 4% of sentences conveyed negative
reflections, while sentences from the Visual group exhibited a slightly lower
2% negativity rate. In stark contrast, the sentences from the Multimodal group
displayed an absence of negative sentiments altogether, echoing the similar
pattern observed in the mood change PANAS, as well as recommendation quality
measures. Figure 13 shows some sample sentences from the positive and negative
reflections of participants per group on a 2D projection map of all reflection
sentences using the non-linear projection t-SNE algorithm (van der Maaten and
Hinton, 2008).

Figure 12.  Sentiment analysis of user reflections per group.  Figure 13.  t-SNE projection of sentence embeddings from user reflections  Themes |  Theme Description |  Example Quotes
---|---|---
Hope and Purpose |  Drawing one’s attention toward more positive prospects, reminding them of hope and purposefulness. |  “The style of the painting makes it more dynamic and the colors also make it more appealing. ... This color palette really brightens my mood and puts me in a better place mentally. ... I felt happy and hopeful about the future. I was in a very good mood.” P07
Rejuvenation |  Supporting one to feel recharged through a sense of being carefree, calm, and relaxed. |  “In this painting, I felt calm, relaxed, and refreshed. ... I felt my spirit being re-energized as I cast away my concerns and worries.” P21
Engagement |  Supporting one to immerse into the visual art by triggering one’s attention and interest. |  “... the shadows and the light are done perfectly to give me that feeling of being there, actually seeing myself walking down the road on my way to the dam. This is a place I know in my mind, thus making the painting very engaging to me.” P35
Safety |  Promoting a sense of safety through elements that signal a safe and familiar environment. |  “It (the scenery in the painting) is very comforting and close to home. The subject matter is also important because it represents a situation where I’m in someone’s company. ... I felt safe and at ease because I’m in good company and surrounded by nature.” P04
Sensory Pleasure |  Providing pleasure through rich sensory stimulation that either directly comes from visual art or is derived from memory through visual triggers. |  “In this painting, I found myself standing on the edge of a serene, sun-kissed meadow, surrounded by vibrant wildflowers swaying gently in the breeze. The colors were so vivid that I could practically feel the warmth of the sun on my skin and the softness of the grass beneath my feet. ... I began to meditate, allowing the beauty of the painting to fill my senses.” P42
Relevance |  Presenting subject matter relevant to one’s specific situation that can stimulate memories or constructive reflections. |  “The scenery and style helped me remember previous times I have been in this situation. It made me think of all the good times and look forward to more good times. It was a very positive way to pass the time and think about what I need to be focusing on in life.” P18
Personal Preference |  Increasing pleasure with visual stimulation that meets one’s preference. |  “... since I love to visit places with a lot of forest, where there is not much noise and if there is noise, it is of nature, this painting helped my experience a lot.” P74
Table 2. Themes and Example Quotes from Some Participants

While the sentiment analysis offered valuable insights, it’s important to
acknowledge that our pre-trained sentiment analysis model based on the
general-purpose language model BERT, may not capture all sentiment subtleties,
particularly those related to healing. Thus, to account for this we further
conducted a qualitative evaluation through reflexive thematic analysis (RTA)
(Braun and Clarke, 2006) to identify healing elements in the recommended arts.
The participant quotes were reviewed and labeled through open coding. These
codes captured information related to the elements that contribute to
patients’ affective states. The results of open coding were re-analyzed to
identify important and general concepts. These concepts were interpreted and
categorized into a total of seven themes based on how they contribute to
eliciting positive emotions and moods: hope, rejuvenation, engagement, safety,
sensory pleasure, relevance, and personal preference (see Table 2 for the list
of themes and example quotes). The identified themes show different elements
that contribute to the healing of individuals, each leading to unique paths to
healing. We observed that these paths involve symbolic associations, such as
hope and purpose for some individuals, while for others, they involve
aesthetic values, such as sensory pleasure or personal preference. Therefore,
the list of these themes can be used as elements to select healing paintings
tailored to the needs of individuals: one who is drained might seek
rejuvenation, while one who feels insecure might seek safety or familiarity.
These findings underpin the importance of personalization of healing art for
therapeutic purposes. Most identified themes, such as engagement, safety,
sensory pleasure, familiarity, and personal preference, echo the relaxing
visual nature elements found in other studies (Kim et al., 2023; Appleton,
1975; Wilson, 1986). Hope and purpose are often emphasized as core elements in
one’s coping and healing process (Allen, 2008). This analysis also revealed
the significance of including these elements in art to function as a healing
mediator. We observed that the absence of such elements had led to rather
negative affective experiences in all three groups (Expert, Visual, and
Multimodal groups). An absence of engagement, for instance, contributed to
boredom: _“This one just doesn’t draw me in, (…) I would find it very boring
and not part of any healing experience“_ (participant 54 from Visual Group).
Likewise, an absence of hope led to feeling tense: _“This image doesn’t really
inspire hope. Instead, it brings pessimistic thoughts. I hope for a more
uplifting and positive atmosphere during my time here”_ (Participant 122 from
Multimodal group). An absence of safety led to feeling gloomy: _“This painting
evokes the feeling of being lost. I felt like there was nowhere to hide so I
just had to face whatever feeling I had”_ (Participant 25 from Expert group).

##  7\. Discussion

Based on our findings, we can answer positively our research question posed at
the beginning of this paper. That is, VA RecSys algorithms can indeed support
the rehabilitation of post-ICU patients using art therapy. This has important
implications in several fronts, as we discuss below.

###  7.1. Personalised visual art as PICS intervention

We have explored the potential of art therapy as a PICS intervention and have
tested VA RecSys as a means to personalize visual art for this goal. Overall,
we found that this comparatively new approach to using art, which combines
narrative techniques with personalized recommendations, allowed participants
to engage with various healing elements in the artworks. Additionally, our
findings show that personalized guided art therapy is effective in temporarily
alleviating negative emotions and enhancing positive emotions as well as
enhancing mood states. This suggests that by increasing its duration and
dosage, it has the potential to address the psychological aspects of PICS as
an intervention, which could potentially result in more lasting effects in
enhancing the affective state of patients. Importantly, we utilized nature-
based artwork in this study. While the use of nature-based artwork to support
former ICU patients is a novel approach, previous studies have demonstrated
the therapeutic effects of nature-based visuals in various forms, ranging from
a real nature view (Ulrich, 1984) to static as well as dynamic versions of
virtual nature (Hung et al., 2022; Malbos et al., 2013; Veling et al., 2021).
The results of our study contribute to the ongoing research efforts in
applying nature-based visuals for therapeutic purposes, demonstrating their
potential to support the healing process of patients and enhance their
psychological well-being. Furthermore, in line with a recent study (Kim et
al., 2023) that has highlighted the influence of personal characteristics on
the impact of visual nature experiences, our study suggests the potential for
a higher level of personalization with the support of RecSys.

Finally, we should mention that the process of guided art therapy in this
study engaged an expert solely during the preparation phase, remaining
independent throughout. This suggests the potential for developing guided art
therapy as an intervention for remote and self-administered use, which could
help alleviate the primary constraints of current PICS interventions, known
for their high demand on healthcare professionals.

###  7.2. Crossing boundaries: VA Recsys - From entertainment to therapy

VA RecSys engines originally emerged as a means to enhance user experience in
the entertainment field. Particularly, recent approaches boosted by machine
learning techniques have undoubtedly demonstrated their potential in
supporting users such as museum visitors and art enthusiasts to discover art
pieces that are tailored to their personal preferences and interests.
Furthermore, their ability to uncover complex semantics embodied within visual
art made them powerful tools to support learning and discovery by exposing
users to novel content. While the art entertainment industry has benefited
from these advancements, our study sheds light on the remarkable potential of
VA RecSys to transcend the space of entertainment, emerging as therapeutic
tools within the healthcare domain. In the field of art entertainment, users
seek diversion, enjoyment or relaxation while service providers strive to not
only enhance user engagement and satisfaction but also drive up revenue. VA
RecSys has long been at the forefront, seamlessly aligning these dual
objectives.

In stark contrast to entertainment, therapy serves a deeper purpose; it is a
journey of healing and self-discovery. The use of art in therapy has been
proven to provide individuals with a unique medium to express complex
emotions, confront traumatic experiences, and embark on the path to recovery.
Art therapy, in particular, has emerged as a powerful tool in the hands of
trained professionals to address a wide range of psychological and emotional
challenges. The key to effective therapy lies in personalization and
relevance. Patients seek a therapeutic experience that resonates with their
individual needs and experiences. Thus, a careful selection of paintings
tailored to the individual patient speaks to their unique circumstances,
fostering self-reflection and healing. By introducing VA RecSys to the domain
of therapy, we have showcased its remarkable potential to assist professionals
not just in curating personalized artworks from a vast selection but also in
delivering precise, tailored treatment to patients.

The intent here is not mere engagement as in the domain of entertainment but
rather the transformation of the individual’s affective state and well-being.
Thus, the adoption of VA RecSys algorithms from entertainment to the context
of therapy requires rigorous quality control before being deployed in a system
facing patients. As informed by our pilot test in Section 5.3 not all top-
performing VA RecSys engines in the entertainment domain were found to be
appropriate for the purpose of therapy. Particularly recommendations from our
text-based engines BERT and LDA tend to contain paintings that feature
contents evoking negative emotions and with potentially harmful consequences.
Therefore, we need to underscore the importance of acknowledging the risks
involved and taking the necessary precautions when adopting these algorithms.
On the contrary, our image-based and fusion-based engines produced paintings
that were deemed appropriate by experts and our results also indicate that
they were even perceived to support healing better than expert-curated
recommendations. However, the promising results we observed may indicate a
potential for a Human-in-the-Loop approach wherein experts fine-tune the
recommendations generated by VA RecSys engines. While experts play a crucial
role in ensuring the quality of paintings, VA RecSys could significantly
reduce their workload (e.g., sifting through thousands of individual paintings
from a database), which is reportedly a concern (Pastores et al., 2019; Chang
et al., 2019), thereby enhancing the potential for scaling up guided art
therapy to bring benefits to more patients. This is nonetheless an exciting
opportunity of Human-AI collaboration for future work.

###  7.3. Looking ahead: Potential of VA RecSys in healthcare beyond PICS
intervention

Our exploration of VA RecSys in the context of PICS is but a glimpse into the
vast potential of this innovation within healthcare. Particularly, in light of
our promising results observed in PICS treatment, one natural extension of
this approach is to implement VA RecSys-assisted visual art into the ICUs (see
footnote 12-a). This could support PICS prevention and the well-being of
patients by providing essential emotional support (e.g., reducing fear and
anxiety) through recommending personalized art.

The adaptability of VA RecSys-assisted art therapy holds promise in areas far
beyond the boundaries of intensive care where the use of visual content as a
positive distraction is already active. For instance, the use of projection
creates a more relaxing experience in an MRI room where patients can get
easily worried and feel discomfort (see footnote 12-b). In the context of
residential care, as another example, a virtual window or digital frames are
used to support cognitive activation and recovery (see footnote 12-c). The
implications of our findings extend the potential of VA RecSys engines in the
intersection of AI and healthcare. The role of VA RecSys engines in
facilitating the use of visual art as a positive distraction is merely the tip
of the iceberg, hinting at a future where technology enables more holistic and
personalized care, amplifying our capacity to heal and connect on a profound
level.

(a)  (b)  (c)

Figure 14. VA RecSys enabled Art-therapy in ICU and beyond. (a) ICU with a
luminous LED ceiling by Philips VitalMind.131313Source: www.philips.nl (b) MRI
room with a projection by Philips Ambient Experience1212footnotemark: 12 (c)
Virtual window and digital frame for Residential Dementia Care, AmbientEcho by
Thoolen et al. 2020 (Thoolen et al., 2020)

##  8\. Limitations and future work

While our study highlights the potential of VA RecSys within a therapeutic
context, we acknowledge certain limitations and chart out promising directions
for future research. Firstly, we have observed significant disparities when
using VA RecSys in therapeutic context compared to its conventional
application in entertainment. While our study has shed light on these
distinctions, it has also highlighted risks associated with therapeutic use,
especially when leveraging text-based models. This may partially be attributed
to the quality of the text data source.

Data quality, underpinning VA RecSys recommendations, plays a pivotal role in
its effectiveness. We have employed artist-curated descriptions of 2,368
paintings from the National Gallery dataset, but it is evident that these
descriptions may not fully encapsulate the intricate affective attributes of
the artworks. Thus, improving these models for therapeutic purposes can
potentially be achieved by curating richer, more comprehensive affective
descriptions. Although this entails substantial content curation efforts and a
thorough evaluation, we believe it is a worthwhile endeavour. For future work
it would also be beneficial to implement tree-based indexing data structures
to scale up more efficiently for larger datasets.

Another limitation is that our current preference elicitation method relies on
users selecting a single preferred painting, which may oversimplify their
preferences. An area ripe for improvement involves allowing users to rate all
the sample paintings that provide comprehensive representations of affective
states (i.e., calmness, restoration, and cheerfulness), thereby capturing to
what extent they resonate towards each affective dimension. By using these
ratings as weights and projecting them into the embedding space, we can refine
recommendation accuracy and granularity. Thus, the development and evaluation
of VA RecSys combining the curation of high-quality data with such preference
weighting mechanisms holds potential to improve the current approach in
therapeutic settings. Additionally, by deriving more personalised content
recommendations that uncover deeper semantics of artworks, this may also
extend current VA RecSys approaches mostly limited in entertainment (Yilma and
Leiva, 2023a; Yilma et al., 2020) to benefit other areas such as education,
blend learning, and discovery of artistic concepts.

As hinted in the above subsection, one particularly promising avenue is the
exploration between humans and AI systems in the context of therapy. Here,
experts can fine-tune VA RecSys-generated recommendations to align them
precisely with individual patient needs. Investigating the dynamics of such
collaborative efforts and developing tools to facilitate expert interventions
could significantly enhance the therapeutic value of VA RecSys. This exciting
direction opens doors to more targeted and personalized therapy experiences,
bridging the gap between technology and human expertise. Furthermore, while we
have gained valuable insights with the current sample (i.e., former patients
with psychological symptoms of PICS), validation with patients exhibiting PICS
symptoms is warranted. Finally, one key challenge lies in comprehending the
reasoning behind the VA RecSys recommendations, which remains a critical
aspect in determining model performance in different contexts. The explanation
of machine learning models has been a longstanding challenge in the field of
AI. Nevertheless, recent strides have been made in the realm of explainable
AI, with emerging techniques and methodologies. Leveraging these innovative
approaches to provide more transparent and interpretable explanations for VA
RecSys recommendations holds substantial promise. This advancement can
facilitate a Human-in-the-Loop approach, empowering experts to refine and
enhance therapy efforts with greater precision.

##  9\. Conclusion

We have studied Machine Learning-based VA RecSys approaches to enable
personalized therapeutic visual art experiences for post-ICU patients. We have
evaluated the relevance of the recommendations for therapeutic purposes as
compared to expert-curated recommendations. Our results suggest that Visual
and Multimodal VA RecSys engines compare favourably with expert-curated
recommendations, indicating a great potential to support the delivery of
personalised and targeted art therapy for PICS prevention and treatment.
Overall, our study marks a significant step towards integrating VA RecSys in
the context of therapy. Considering future research directions, this work
points to the exciting potential for further advancements in the field of AI-
assisted therapy and recommendation systems. The implications of our findings
extend to patient-centred care, early intervention, and health promotion.

###### Acknowledgements.

We thank Thomas Falck, MSc (Philips) and Dr. Esther van der Heide (Philips)
for their advice on PICS research, and Prof. Dr. Geke Ludden (University of
Twente) and Dr. Thomas van Rompay (University of Twente) for their advice on
developing guided art therapy used in this study. We extend our thanks to the
participants of our study for sharing their valuable experiences, and to the
anonymous reviewers for their constructive comments. This work was supported
by the Horizon 2020 FET program of the European Union through the ERA-NET
Cofund funding grant CHIST-ERA-20-BCI-001 and the European Innovation Council
Pathfinder program (SYMBIOTIK project), and the Top Technology Twente
Connecting Industry program (TKI Topsector HTSM), which is partially funded by
Philips.

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