# Self-supervised learning-based general laboratory progress pretrained model
for cardiovascular event detection

Li-Chin Chen1 Kuo-Hsuan Hung1 Student Member, IEEE, Yi-Ju Tseng2 Member, IEEE,
Hsin-Yao Wang3 Tse-Min Lu4,5,6 Wei-Chieh Huang4,6,7 Yu Tsao1 Senior Member,
IEEE

###### Abstract

Objective: Leveraging patient data through machine learning techniques in
disease care offers a multitude of substantial benefits. Nonetheless, the
inherent nature of patient data poses several challenges. Prevalent cases
amass substantial longitudinal data owing to their patient volume and
consistent follow-ups, however, longitudinal laboratory data are renowned for
their irregularity, temporality, absenteeism, and sparsity; In contrast,
recruitment for rare or specific cases is often constrained due to their
limited patient size and episodic observations. This study employed self-
supervised learning (SSL) to pretrain a generalized laboratory progress (GLP)
model that captures the overall progression of six common laboratory markers
in prevalent cardiovascular cases, with the intention of transferring this
knowledge to aid in the detection of specific cardiovascular event. Methods
and procedures: GLP implemented a two-stage training approach, leveraging the
information embedded within interpolated data and amplify the performance of
SSL. After GLP pretraining, it is transferred for target vessel
revascularization (TVR) detection. Results: The proposed two-stage training
improved the performance of pure SSL, and the transferability of GLP exhibited
distinctiveness. After GLP processing, the classification exhibited a notable
enhancement, with averaged accuracy rising from 0.63 to 0.90. All evaluated
metrics demonstrated substantial superiority (_p_ $<$ 0.01) compared to prior
GLP processing. Conclusion: Our study effectively engages in translational
engineering by transferring patient progression of cardiovascular laboratory
parameters from one patient group to another, transcending the limitations of
data availability. The transferability of disease progression optimized the
strategies of examinations and treatments, and improves patient prognosis
while using commonly available laboratory parameters. The potential for
expanding this approach to encompass other diseases holds great promise.

Clinical impact: Our study effectively transposes patient progression from one
cohort to another, surpassing the constraints of episodic observation. The
transferability of disease progression contributed to cardiovascular event
assessment.

Cardiovascular diseases, cardiometabolic disease, disease progression,
laboratory examinations, time-series data, pre-train model, representation
learning, self-supervised learning, transfer learning.

11footnotetext: 1 Research Center for Information Technology Innovation,
Academia Sinica, Taipei, Taiwan.22footnotetext: 2 Department of Computer
Science, National Yang Ming Chiao Tung University, Hsinchu,
Taiwan.33footnotetext: 3 Department of Laboratory Medicine, Chang Gung
Memorial Hospital at Linkou, Taoyuan City, Taiwan.44footnotetext: 4 Division
of Cardiology, Department of Internal Medicine, Taipei Veterans General
Hospital, Taipei, Taiwan.55footnotetext: 5 Department of Health Care Center,
Taipei Veterans General Hospital, Taipei, Taiwan.66footnotetext: 6 Department
of Internal Medicine, School of Medicine, College of Medicine, National Yang
Ming Chiao Tung University, Taipei, Taiwan.77footnotetext: 7 Department of
Biomedical Engineering, National Taiwan University, Taipei, Taiwan.

##  1 Introduction

Regular surveillance stands as an imperative facet within cardiovascular
disorders management [1]. Laboratory analysis constitutes a vital component,
involving multifarious chemical tests that scrutinize blood, urine, or body
tissue specimens. These tests gauge the body’s response to food intake,
medication, and treatment, thus providing crucial insights into disease
progression and signaling the need for medication or dietary modifications.
For chronic diseases, laboratory results are more meaningful when observed
longitudinally rather than episodically. A vast repository of longitudinal
data has been accumulated for prevalent diseases such as hypertension (HTN)
and diabetes mellitus (DM). Nonetheless, the inherent nature of these data,
characterized by irregularity, absenteeism, and sparsity, presents challenges
in leveraging their full potential for machine learning applications.
Conversely, rare or specific cases are frequently associated with a limited
patient population and episodic observations, also impeding the integration of
machine learning technology in their progression assessment. This study
employed self-supervised learning (SSL) to pretrain a generalized laboratory
progress (GLP) model. GLP captures the overall progression of common
laboratory markers in prevalent cardiovascular cases, with the intention of
transferring this knowledge to aid in the detection of target vessel
revascularization (TVR) occurrences in patients undergoing percutaneous
coronary intervention (PCI).

### Challenging nature of laboratory data

Diverse methodological approaches can be adopted depending on the distinct
characteristics of the population under study. Cross-sectional studies, which
capture the patient status at a single time point, provide only a temporary
snapshot and preliminary glimpses into future disease progression. In
contrast, cohort studies, with their longitudinal observations, offer a more
comprehensive understanding of disease development [2]. However, the
collection of such data over an extended period can be intricate, time-
consuming, and costly, frequently suffered from patient dropouts and
incomplete data [3, 4].

Consequently, rare or specific diseases frequently resort to cross-sectional
studies due to limited patient size, yielding episodic observations. In the
context of prevalent cases, longitudinal data is more readily accessible owing
to continuous follow-ups. Nonetheless, these observations heavily rely on
patient adherence, insurance regulations, clinical guidelines, and the
clinical judgment of physicians. Any disruption to these factors can lead to
irregularity and sparsity, where observations may be skipped, or sampled
irregularly over a prolonged period [5, 6]. Similarly to electronic health
records (EHRs), laboratory test records encompass a wealth of abundant and
longitudinal patient information, still, notable for their irregularity,
temporality, and sparsity, often accompanied by noisy outliers and missing
values [3, 4, 6].

### Related works

Machine learning techniques have undergone extensive investigation,
facilitating diverse applications that contribute to clinical care through the
utilization of EHRs [7, 8, 9]. SSL has recently gained attention due to its
ability to derive labels for training data directly from the data itself [10,
11, 12, 13, 14]. This offers a unique opportunity to leverage the vast amounts
of data available without relying on quality annotations [15, 16, 13].
Generally, SSL can be categorized into generative and contrastive learning
approaches [17, 18]. Generative models possess the capability to generate new
samples from the underlying distribution or recover the original data
distribution [13, 19]. For instance, Simone et al. [19] showcased the
utilization of a generative adversarial network (GAN) for the synthesis of
electrocardiography (ECG) data. Their study achieved remarkable results by
generating a wide spectrum of ECG patterns that preserved synchronization and
abnormalities. Yoon et al. [14] leveraged SSL strategies to impute corrupted
values and train on unlabeled data in the domain of genomics and clinical
data. Furthermore, Lee et al. [20] employed GPT-4 to summarize physician-
patient conversations and generated clinical notes.

Meanwhile, contrastive learning aims to capture the relationship between input
data and prediction targets, thereby generating a global contextual
representation that is shared among samples [13, 16]. For instance, Kiyasseh
et al. [16] devised a temporal and spatial discriminative approach for ECG
analysis, extracting patient-specific representations through leveraging
contrastive loss. Zhang et al. [21] employed contrastive learning to capture
the distances between temporal and frequency components, and applied the
pretrain representations to various time-series databases, including ECG,
human activity recognition, and physical status monitoring. Wickstrøm et al.
[22] proposed a contrastive framework based on mixing up augmentation for uni-
and multivariate time-series data, which was then transferred to ECG
classification. Furthermore, Ouyang et al. [23] trained an encoder using
unlabeled retinal images through contrastive learning, and subsequently fine-
tuned the encoder for the classification of reference and non-reference cases.

Many SSL applications focus on learning from a pretext task and transferring
the learned representations to a different domain. This entails transfer
learning, a concept that improves a classifier in one domain (pretext task)
using more readily obtainable data and then applying this acquired knowledge
to another domain (downstream task) [24]. For instance, Tang et al. [25]
utilized a teacher-student self-training model to capture information from a
large-scale unlabeled dataset of wearable and mobile sensing data, which was
subsequently transferred to seven different datasets with varying sensor
types, populations, and protocols. Similarly, Spathis et al. [26] used
activity accelerometer sensor data as input to forecast heart rate and
transferred the learned representations to capture physiologically meaningful
and personalized information using linear classifiers.

Nontheless, the aforementioned works assume that signals and information were
collected on a regular basis and do not address the issues of irregularity,
absenteeism, and sparsity commonly encountered in EHRs. Tipirneni et al. [27]
bypass the irregular, absent, and sparse nature of EHRs by training the model
in a SSL manner and mask out unobserved forecasts in the loss function during
training. Furthermore, SSL applications have been recognized as challenging in
terms of finding effective pretrain tasks [17]. Most studies have relied on an
empirical trial-and-error approach to identify the most suitable pretext
tasks. The transferability of pretrain models has shown mixed results when
applied to more specific domains such as medicine [28]. For instance, Liu et
al. [29] were unable to successfully transfer ImageNet for detecting lymph
node metastasis in pathology images, citing significant domain differences
between natural scenes and pathology images as the reason for the transfer
failure. Another example is the discussion surrounding language representation
models for the biomedical domain [30]. Gu et al. [31] argued that models
trained on domain-specific vocabulary outperform those trained on general
corpora due to differences in word distributions between general and
biomedical corpora .

### Cardiovascular diseases and associated risk factors

Cardiovascular disease stands as one of the leading causes of global
mortality. Established traditional risk factors incorporate HTN, DM, and
smoking [32]. These risk factors can contribute to endothelial injury, plaque
formation, and coronary thrombus formation [33], consequently driving the
progression of cardiovascular disease. PCI has emerged as a widely employed
treatment modality for cardiovascular disease [34].

Although the implementation of drug-eluting stents (DES) has significantly
reduced the incidence of TVR in recent years [35, 36], the occurrence of TVR
after DES implantation, with an incidence ranging from 3% to 20%, remains a
prevalent clinical concern [37, 38, 39]. Hence, the prevention of TVR and the
reduction of re-admission rates continue to be significant clinical challenges
in the field of cardiovascular medicine following PCI. With respect to the
timing of cardiac events, patients undergoing PCI face a risk of subsequent
adverse events, including TVR [40, 41]. However, achieving a consensus on
accurate preprocedural risk stratification and prognosis assessment to
identify high-risk patients prior to PCI remains an ongoing pursuit.

TVR is associated with complex pathophysiological mechanisms involving lipid
metabolic disorders [42] and inflammatory processes [43]. Several previous
studies have explored potential predictive factors linked to a high incidence
of TVR based on patient and procedure-related variables [44, 45, 46]. However,
dedicated applications for TVR prediction are yet to be developed. Most
studies have focused on identifying comprehensive predictors for TVR or
developing prediction models without specifically targeting individualized
risks [45, 47, 48]. The collection of data for comprehensive predictors may
introduce burden and complexities when integrating such applications into
routine clinical practice and thus warrants careful consideration.

### Laboratory markers of cardiovascular diseases

Earlier studies have indicated that preprocedural parameters are associated
with cardiovascular disease. Total cholesterol levels (Chol) and low-density
lipoprotein cholesterol (LDL-c) are strongly linked to cardiometabolic
diseases and widely accepted in diagnostic practices. Conversely, the plasma
level of high-density lipoprotein cholesterol (HDL-c) exhibits an inverse
relationship with the risk of cardiovascular diseases [49]. Clinical studies
have highlighted the connection between circulating white blood cells (WBCs)
and cardiovascular outcomes, demonstrating that elevated WBC count increases
the short- and long-term risk in patients with acute coronary syndromes (ACSs)
[50]. Hage et al. [51] reported that baseline fasting blood glucose (glucose
AC) predicts restenosis, suggesting that focusing on glucose reduction rather
than solely normalizing glucose levels is more beneficial [51]. Additionally,
serum uric acid (UA) has been identified as a prognostic cardiovascular
biomarker, predicting total and cardiovascular mortality in the context of
secondary prevention of coronary artery disease, as demonstrated by the Verona
Heart Study [52]. Furthermore, the National Cholesterol Education Program III
(NCEP III) recommends the use of Chol or LDL-c in conjunction with HDL-c
(Chol/HDL-c, LDL-c/HDL-c) as markers for screening and treating patients with
cardiovascular disease, along with the utilization of the 10-year risk
Framingham scoring assessment [53].

##  2 Methodology

To address the challenges previously mentioned, we have devised the following
propositions for our work: (1) In order to address the challenges stemming
from irregularity, absenteeism, and sparsity within longitudinal observations,
we have deployed interpolation and SSL techniques to infer absented data. (2)
For patients with limited numbers and episodic observations, we developed a
pretrain model specifically tailored to capture the temporal latent
representation of prevalent cases and transfer disease progress knowledge to
these smaller cohorts. (3) Our work is based on commonly available resources,
avoiding the need to initiate new trials for extensive patient data
collection. We focused on six laboratory parameters: the Chol/HDL-c ratio,
LDL-c, the LDL-c/HDL-c ratio, glucose AC, WBC, and UA. (4) Our work leverages
the intercorrelation among cardiometabolic diseases as an indication of
pretext and downstream tasks.

Our objective is to construct a two-stage pretraining model that captures the
laboratory progress of general cases and utilizes this information to predict
cardiac events in another patient group. Prior research has indicated that
incorporating a two-step training approach, involving pretraining the model on
a domain-general dataset followed by training on domain-specific datasets,
yields enhancements in transitioning representations to the downstream task
[12, 30, 23]. Hence, we propose a two-stage training process: Stage 1 involves
learning general laboratory progress information based on interpolated data,
followed by Stage 2, where SSL is employed to refine the model’s progression
representation using non-interpolated data. Subsequently, GLP model is fine-
tuned to classify the occurrence of TVR.

The following sections outline the detailed methodology of GLP. Initially, we
introduced the interpolation and framing method for longitudinal data.
Subsequently, we outlined the design of the GLP model, along with the training
algorithm and the design of downstream classifier. We also elucidated the
validation methods employed for both the pretext and downstream tasks. Lastly,
we provided details regarding the patient recruitment process and the datasets
utilized for both tasks.

### Interpolation methods

Interpolation serves the purpose of inferring values that lie between two
known observations. It aims to approximate the values of $\hat{f}(x)$ that
fulfill the interpolation conditions $\hat{f}(x_{j})=y_{j}$ for
$j=0,1,\ldots,n$. This study encompassed the evaluation of three interpolation
techniques: linear interpolation, piecewise cubic Hermite interpolating
polynomial (PCHIP), and barycentric interpolation. The values in linear
interpolation were derived by considering the gradients of the known
observations, denoted as:

$\hat{y}_{j}=y_{i}+(t_{j}-t_{i})\frac{(y_{k}-y_{i})}{(t_{k}-t_{i})},$ (1)

where $t$ signifies the time of estimation, $\hat{y}_{j}$ denotes the
estimated value at $t_{j}$, and $i<j<k$. Meanwhile, PCHIP interpolation [54]
defines $d_{j}=(y_{k}-y_{j})/(t_{k}-t_{j})$ as the slopes at $x_{j}$. If the
signs of $d_{j}$ and $d_{i}$ differ or either of them equals zero,
$\hat{y}_{j}$ is set to 0. Otherwise, it is determined using the weighted
harmonic mean, expressed as:

$\hat{y}_{j}=\frac{(w_{1}+w_{2})}{\frac{w_{1}}{d_{i}}+\frac{w_{2}}{d_{j}}},$
(2)

where $w_{1}=2(t_{k}-t_{j})+(t_{j}-t_{i})$ and
$w_{2}=(t_{k}-t_{j})+2(t_{j}-t_{i})$. Finally, for barycentric interpolation
[55, 56], a given set of nodes $x_{0},x_{1},\ldots,x_{n}$ and masses
$w_{0},w_{1},\ldots,w_{n}$ are utilized to determine the functions
$w_{0}(x),w_{1}(x),\ldots,w_{n}(x)$ satisfying:

$x=\frac{\sum^{n}_{i=0}w_{i}(x)x_{i}}{\sum^{n}_{i=0}w_{i}(x)}.$ (3)

Here, $x$ represents the barycenter of the nodes, which can be employed for
interpolation using:

$\hat{y}=\sum^{n}_{i=0}b_{i}(x)f_{i},$ (4)

where $b_{i}$ corresponds to the linear function. Fig. 1 illustrates the
segmented period of glucose AC, employing different interpolation methods.

Figure 1: Interpolation results based on different methods. This showcases a
segmented period of glucose AC. The x-axis corresponds to the timeline, while
the y-axis represents the laboratory values.

### Longitudinal data framing

By defining the laboratory observations for each patient during the study
period as $y_{t_{0}},y_{t_{1}},\ldots,y_{t_{n}}$, where $t_{i}$ represents the
months in the timeline and $t_{i}\in\mathbb{R}$, we designate $y_{t_{0}}$ and
$y_{t_{n}}$ as the actual observed values. Let $y_{t_{m}}$ denote the second-
to-last observed value of a patient. Interpolation takes place between
$y_{t_{0}}$ and $y_{t_{m}}$ when $y_{t_{i}}$ is missing. The observations were
organized into a frame with a designated time interval $r$. Therefore, the
longitudinal data is framed as $y_{t_{i}}:y_{t_{i+r}}$, with subsequent frames
incrementing by one step ($i+1$) while $i+r\leq m-1$. If $\lVert
t_{i},t_{m-1}\rVert\leq r$, the frame is omitted. These segmented frames,
denoted as the interpolated data, serve as the input of Stage 1. Their
corresponding prediction target is $y_{t_{i+r+1}}$, where $i+r+1\leq m$.

From the previous stage, the frame $y_{t_{m-r}}:y_{t_{m}}$ and the last
observed value $y_{t_{n}}$ are isolated. They are the non-interpolated data in
Stage 2, with $y_{t_{m-r}}:y_{t_{m}}$ serving as the input and $y_{n}$ as the
prediction target. Considering that coronary stent trials primarily focus on
target vessel/lesion-related clinical outcomes within the shorter term,
particularly in the first 12 months post-PCI [57], we set $r$ as 12 months.
The framing process is visually depicted in Figure 2.

Figure 2: Logitudinal data framing for two-stage training. SSL: self-
supervised learning; $r$: time interval (frame size); $g$: time gap between
the input frame and prediction target, where $g\leq\frac{r}{2}$.

### GLP model design

Figure 3: GLP model design. $g$: time gap between the input frame and
prediction target; $Progress_{emb}$: latent output of $LIBC$;
$Progress_{out}$: latent output of $regressor$.

GLP was designed to monitor the laboratory progress of patients and forecast
their observations for the subsequent month. Fig. 3 presents the model
architecture of GLP, which consists of two main components: the longitudinal
iterative block ($LIBC$) and a $regressor$. The $LIBC$ comprises a
Bidirectional Long Short-Term Memory (BiLSTM) layer and a Fully Connected (FC)
condensing layer. A Rectified Linear Unit (ReLU) activation function is
applied after each layer to enhance the non-linearity of the model. The BiLSTM
processes the input data in both the forward and backward directions [58],
enabling the capture of contextual information from past data. The number of
hidden nodes for the BiLSTM layer was set to 5. The FC condensing layer is
utilized to compress the output of the BiLSTM layer back to the original
input, facilitating an autoregressive flow.

On the other hand, the $regressor$ consists of two FC layers followed by a
ReLU activation function in between. The number of hidden nodes for the
$regressor$ is set as 5, 2, 2, and 1, respectively. The model used the Mean
Squared Error (MSE) as loss function, which measures the average squared
difference between the predicted values and the actual values.

### Two-stage training algorithm

During Stage 1 training, the interpolated data were utilized in supervised
learning. An additional parameter called the number of certainty mask
($certain$) was introduced in Stage 1. It denotes the required number of real
observations within a frame, indicating the level of real observations the
models require to generate reliable predictions. It also serves as a means to
address uncertainty within the training data. Adhering to insurance
regulations, patients were scheduled for cardiovascular disease examinations
every three months, resulting in a maximum of four actual observations within
a 12-month timeframe. Consequently, the range of 0 to 5 was explored for
$certain$, and the optimal value was determined as a parameter setting for
GLP.

In Stage 2 training, we employed an autoregressive based SSL, which takes
inputs from a time series regressed on previous inputs from the same time
series. The probability of each input is conditioned on the preceding input,
and can be formulated as:

$\max_{\theta}p_{\theta}(x)=\sum_{t=1}^{T}\log p_{\theta}(x_{t}\mid
x_{1:t-1}),$ (5)

where $x_{t}$ represents the input at time $t$, $p_{\theta}$ denotes the
probability, and $\max_{\theta}p_{\theta}$ signifies the maximized likelihood
[13]. Starting from frame $y_{t_{m-r}}:y_{t_{m}}$, the model utilizes the
parameters $\theta$ obtained from Stage 1 and predicts $y_{t_{m+1}}$. The
subsequent input frame becomes $y_{t_{m-r+1}}:y_{t_{m+1}}$, with the
prediction target located at $y_{t_{m+2}}$. This process continues until the
prediction target reached $y_{t_{n}}$. The predicted data then becomes part of
the training data for the next frame, allowing the model to learn from its own
generation.

To summarize, in Stage 1, there is no time gap ($g=0$) between the input frame
and the prediction target. The input passes through the $LIBC$ once and then
enters the $regressor$. On the other hand, in Stage 2, $g=n-m-1$ and $g>0$,
and the process iterates until the prediction target is reached. The training
algorithms for Stage 1 and 2 are explicitly outlined in Algorithm 1 and 2.
Each laboratory parameter was trained individually and optimized to achieve
the best performance. The six pretrained GLP models were subsequently
concatenated in a multimodal fashion and utilized for domain transfer to
perform TVR occurrence classification, as illustrated in Figure 4.

Algorithm 1 Stage 1 training

Input: $X_{interpolated}$, batch size $N$

1:for $epoch=0$ to $50$ do

2: for $batch\\{x_{k}\\}^{N}_{k=1}\in X_{interpolated}$ do

3: pass $x_{k}$ through $LIBC$ and $regressor$

4: Optimize using Adam optimizer

5: end for

6: Save the parameters $\theta$ of the model

7:end for

Output: $\hat{y}$

Algorithm 2 Stage 2 training

Input: $X_{non-interpolated}$, batch size $N$, time gap between the input
frame and prediction target $g$, parameters $\theta$ from stage 1 training

1:for $epoch=0$ to $50$ do

2: for $batch\\{x_{k}\\}^{N}_{k=1}\in X_{non-interpolated}$,
$\\{g_{k}\\}^{N}_{k=1}\in g$ do

3: if $max(g_{k})=0$ then

4: pass $x_{k}$ through $LIBC_{\theta}$

5: end if

6: if $max(g_{k})>0$ then

7: for $t\in\\{1,2,\ldots,max(g_{k})\\}$ do

8: pass $x_{k}$ through $LIBC_{\theta}$

9: end for

10: for $g_{i}\in\\{1,2,\ldots,k\\}$ do

11: select estimated $\hat{y_{k}}$ at time stamp $g_{i}+1$

12: end for

13: end if

14: pass $\hat{y_{k}}$ through $regressor_{\theta}$

15: Optimize using Adam optimizer

16: end for

17: Update the parameters $\theta$ of the model

18:end for

Output: $\hat{y}$

Figure 4: Training pipeline. GLP: general laboratory progress pretrain model;
Chol/HDL-c: ratio of total cholesterol and high-density lipoprotein
cholesterol; LDL-c: low-density lipoprotein cholesterol; LDL-c/HDL-c: ratio of
low-density and high-density lipoprotein cholesterol; Glucose AC: fasting
blood sugar; WBC: white blood cells; UA: Uric Acid; PCI: percutaneous coronary
intervention; TVR: target vessel revascularization.

### Input vector and normalization

The input of GLP consists of five features: age, gender, $certain$, discrete
value encoding, and normalized laboratory values. Numeric values, such as age
and laboratory values, are normalized using the natural logarithm of one plus
the input ($log1p$). This transformation ensures that the values are projected
into a vector space above zero, preventing potential errors that could arise
from maldistribution between positive and negative values. Additionally,
$log1p$ is accurate for small values of $x$, ensuring that $1+x=1$ with
floating-point accuracy without significantly altering the original value
[59]. The use of $log1p$ also prevents information leakage [60], unlike
scaling which necessitates knowledge of the maximum and minimum values.

One-hot encoding is employed for gender, certainty mask, and discrete value
encoding. Gender is binary-encoded, with a value of 1 assigned to male and 0
assigned to female. The $certain$ is also binary-encoded. It indicated whether
the value is a real observation or an estimation obtained through
interpolation (true observation/estimated value). Discrete value encoding
categorizes laboratory results into two groups (low and high) or three groups
(low, normal, and high) based on different threshold values (summarized in
Appendix 7).

### GLP validation

The validation of GLP was conducted by forecasting $y_{n}$ without
interpolation support within $\frac{r}{2}$. To further assess the performance
of the proposed two-stage training process, we used SSL as baseline (Stage 2
training), and compared with the following approaches: (1) Supervised training
based on interpolated data (Stage 1 training). (2) Hybrid training, which
combines interpolated and non-interpolated data, and performs supervised and
self-supervised training simultaneously. (3) Two-stage training, where GLP
first reaches a local minimum loss using interpolated data and then undergoes
domain-specific adjustments using SSL.

Regardless of the employed training approaches, the data were randomly divided
into training and testing datasets in an 80:20 ratio. All training processes
utilized the 5-fold cross-validation technique, and the reported results
represent the mean value obtained from five repetitions of the training
process [61]. Ablation studies were conducted to analyze the performance of
different training processes combined with various interpolation methods. The
outcome of the model was assessed using the R-squared ($R^{2}$) metric, which
indicates the proportion of variance in the dependent variable that can be
predicted by the independent variables in the model. $R^{2}$ values range from
zero to one, with a value of one representing a perfect fit to the data and a
value of zero indicating a poor fit. $R^{2}<0$ suggests that the model
performs worse than a horizontal mean line passing through the mean value of
the data. Statistical significance to determine differences in model
performance was assessed using an independent T-test, with _p_ $<$ 0.05
indicating statistical significance.

### Downstream classifier design and validation

The collection of the six laboratory values occurred at the time of performing
PCI. Subsequently, $g$ (month-based) was computed based on the temporal
disparity between the PCI and TVR dates. Patient information, including
gender, age, and the six laboratory parameters, was collected and normalized
following the aforementioned procedures. Due to the imbalanced distribution of
patients between those with TVR occurrence (42) and those without (441), TVR-
negative cases were randomly downsampled. As a result, only 84 patients
entered the training process. Patient data are processed by the frozen GLPs,
which are concatenated in a multimodal fashion (as shown in Figure 4).

Owing to the limited patient volume, non-neural network algorithms were chosen
for training the downstream classifier. The included methods are Light
Gradient Boosting Machine (LightGBM), Support Vector Machine (SVM), Logistic
Regression (LR), and K-Nearest Neighbors (KNN). These approaches were selected
for their diverse mechanisms. Cohen’s Kappa was calculated to assess the
agreements between the classifiers, and the mean value of Cohen’s Kappa
represented the overall agreements among the pairwise classifiers. A value of
Kappa $\leq$ 0 indicates no agreement, while an increase in Kappa signifies an
increase in agreement, with a value of 1 indicating perfect agreement.

Additionally, we compared the latent progress representations produced by GLP
by extracting the outputs of $LIBC$ ($Progress_{emb}$) and $regressor$
($Progress_{out}$), depicted in Figure 3. The transferred results based on the
original data (normalized but not processed with GLP), $Progress_{emb}$, and
$Progress_{out}$ were compared. The reported performance represents the mean
value obtained after executing the training process five times. The downstream
classifier is a binary classifier that distinguishes TVR occurrence
(positive/negative). The evaluation metrics for TVR incidence include the area
under the receiver operating characteristic curve (AUROC), accuracy,
sensitivity, specificity, precision, and F1 score. To determine the
statistical significance of the differences in evaluation metrics between the
original data and the extracted representations, an independent T-test was
also conducted.

Furthermore, we plotted the distribution of the original data,
$Progress_{out}$ at $g/2$, and $Progress_{out}$ at $g$ to visually depict the
contribution of GLP throughout the process. Here, $g/2$ signifies that the
iteration before reaching the intended event.

### Patient recruitment and datasets

Two datasets were obtained from two diverse medical institutes. The pretext
dataset was obtained from the Chang Gung Research Database [62], a multi-
institutional electronic medical records database comprising original medical
records of seven medical institutes in Taiwan. We included patients who were
diagnosed with HTN prior to DM. Patients diagnosed with hypertension before
the age of 40, those with any oncology visits, or individuals with
observations spanning less than a year were excluded. The date of diagnosis
was determined based on the International Classification of Diseases (ICD)
encoding or the date of medication prescription. The ICD codes and medications
used for the indications can be found in Appendix 6. When a patient had been
coded as having HTN or DM twice or more during a year, the onset date of the
disease was defined as the first coded date. If the date of the first
medication prescription preceded the ICD coded date, then the earlier date was
designated as the onset date of the disease. The time interval between the HTN
and DM diagnoses was set to be $\geq$ 3 months.

We gathered data on patients between the onset of HTN and DM; that is, the
patient was diagnosed with HTN, but yet to be determined as DM. Demographic
information and laboratory data of the enrolled patients were collected,
including age, sex, Chol/HDL-c, LDL-c, LDL-c/HDL-c, glucose AC, WBC, and UA.
Erroneous values such as ”NA” or ”.” were excluded. A total of 9,720 patients
were included, and laboratory data were collected between January 2001 and
December 2019.

The downstream dataset was acquired from the Taipei Veterans General Hospital,
a tertiary hospital situated in northern Taiwan. We recruited 891 patients
with noninvasive evidence of myocardial ischemia who underwent PCI between
January 2005 and January 2022. Patients with ACS, acute decompensated
congestive heart failure, acute or chronic infections, autoimmune diseases,
malignancies with a prognosis of less than one year, unstable hemodynamic
status, or those unable to receive dual antiplatelet therapy were excluded.
Angiographically successful coronary intervention was defined as residual
stenosis of less than 30%, and coronary thrombolysis in myocardial infarction
grade 3 flow was achieved at the conclusion of the procedure without any
significant complications. All patients were followed up and to monitor the
occurrence of TVR.

Patients necessitating TVR were labeled as positive, and the corresponding
dates were recorded. Patients without TVR until the end of January 2022 were
classified as negative, with the date of January 31, 2022 recorded as the
endpoint. Within this dataset, the available information encompassed age, sex,
PCI date, TVR date, and the aforementioned six laboratory values (collected
during the PCI procedure). The time intervals between the PCI and TVR dates
were calculated. Patients with incomplete data were excluded from the analysis
to avoid deviations into this study, as the imputation of missing values could
potentially have that effect. Consequently, a total of 483 patients were
included in the subsequent analysis, comprising 42 TVR and 441 non-TVR cases.

The pretext dataset are longitudinal observations that consists of multiple
events, whereas the downstream dataset are episodic records that consists of
one observation event for each patient. All patient data were de-identified
prior to analysis. This study was approved by the Institutional Review Board
of the Chang Gung Medical Foundation (No. 202000376B0) and Taipei Veterans
General Hospital (No. 2019-12-012CC).

##  3 Results

Table 1 illustrates the demographic information of the patients enlisted from
the two datasets. It is noteworthy that patients in the pretext dataset (HTN
to DM patients) exhibit a relatively younger age compared to those in the
downstream dataset (PCI patients). It is observed that TVR typically occurs
within a period of 2.32 $\pm$ 2.64 years. Fig. 5 depicted the overall
performance of GLP, assessed by averaging the $R^{2}$ results across $certain$
ranging from 0 to 5. Fig. 5(a) provides an overview of the general disparities
among different training approaches, incorporating all interpolation methods.
It reveals that SSL and two-stage training exhibit similar performances, both
achieving a mean $R^{2}$ value of 0.46 when rounded to the second decimal
place. On the other hand, supervised training (Stage 1 training) and hybrid
training achieved lower means with larger variations.

Fig. 5(b) to Fig. 5(d) delves into the exploration of two-stage training,
supervised training, hybrid training, respectively. All figures inspect into
the effect of different interpolation methods. Observing from the figures, the
two-stage training approach (Fig. 5(b)) is the only approach that surpasses
SSL (baseline). Using linear ($R^{2}$ = 0.49, _p_ = 0.508) and PCHIP ($R^{2}$
= 0.48, _p_ = 0.603) both yield better results than SSL, although the
differences did not reach statistical significance. However, when compared to
barycentric interpolation, both linear (_p_ = 0.031) and PCHIP (_p_ = 0.046)
show significantly higher performance. Other training approaches, including
supervised training depicted in Fig. 5(c) and hybrid training in Fig. 5(d),
demonstrate weaker performance compared to the horizontal mean line ($R^{2}<$
0), with larger variations.

Table 1: Demographic information of the recruit patients Items | Values
---|---
HTN to DM patient (Pretext dataset) |  Gender (n, %) |  Male |  5032 (0.518)
Female |  4688 (0.482)
HTN onset age (mean, SD) |  56.965 |  9.653
DM onset age (mean, SD) |  61.884 |  9.878
Duration between HTN and DM (year, mean, SD) |  4.992 |  3.945
PCI patients (Downstream dataset) |  Gender (n, %) |  Male |  402 (0.832)
Female |  81 (0.168)
PCI treatement age (mean, SD) |  66.828 |  12.471
TVR occurrence (n, %) |  Yes |  42 (0.087)
No |  441 (0.913)
Duration between PCI and TVR occurrence (year, mean, SD) |  8.277 |  4.088
Occurrence duration (year, mean, SD) |  2.319 |  2.640
Non-occurred duration (year, mean, SD) |  8.844 |  3.734
n: number of samples; SD: Standard deviation; HTN: hypertension; DM: diabete
mellitus; PCI: percutaneous coronary intervention; TVR: target vessel
revascularization. Duration between HTN and DM signifies the cumulative
observed timeframe for each individual. Occurrence duration delineates the
temporal interval between PCI and TVR, and non-occurred duration characterizes
the observed time span for patients who did not experienced TVR.

Figure 5: $R^{2}$ values for different training approaches. SSL (baseline) is compared with (a) averaged $R^{2}$ across different training approaches; (b) two-stage training employing different interpolation methods; (c) supervised training employing different interpolation methods; and (d) hybrid training employing different interpolation methods. The mean value is represented by the green triangle and its corresponding numerical figure is provided in the text below. The presented outcomes are an average of five repetitions of training and prediction, determined by aggregating the $R^{2}$ outcomes across $certain$ values ranging from 0 to 5. SSL: self-supervised learning. Table 2: $R^{2}$ of GLP based on optimized certainty mask configurations |  $certain$ |  SSL |  Two-stage |  _p-value_
---|---|---|---|---
Linear
Chol/HDL-c |  5 (2.4 months) |  0.509 |  0.602 |
LDL-c |  3 (4 months) |  0.373 |  0.376 |
LDL-c/HDL-c |  3 (4 months) |  0.515 |  0.522 |
Glucose AC |  4 (3 months) |  0.648 |  0.666 |
WBC |  4 (3 months) |  0.778 |  0.774 |
UA |  4 (3 months) |  0.353 |  0.454 |
Avg. |  3.8 (3.1 months) |  0.529 |  0.566 |  0.773
PCHIP
Chol/HDL-c |  5 (2.4 months) |  0.392 |  0.586 |
LDL-c |  3 (4 months) |  0.367 |  0.341 |
LDL-c/HDL-c |  2 (6 months) |  0.505 |  0.521 |
Glucose AC |  4 (3 months) |  0.644 |  0.602 |
WBC |  5 (2.4 months) |  0.751 |  0.779 |
UA |  5 (2.4 months) |  0.316 |  0.373 |
Avg. |  4 (3 months) |  0.496 |  0.534 |  0.702
Chol/HDL-c: ratio of total cholesterol and high-density lipoprotein
cholesterol; LDL-c: low-density lipoprotein cholesterol; LDL-c/HDL-c: ratio of
low-density and high-density lipoprotein cholesterol; Glucose AC: fasting
blood sugar; WBC: white blood cells; UA: Uric Acid. The differitation did not
reach the level of statistical significance (_p_ $>$ 0.05). The month
indicated in the brackets signifies the necessary follow-up visits for
patients based on the requisite count of actual observations within a 12-month
period.

Table 2 provides a summary of the optimal $certain$ settings for each GLP,
which were found to be inconsistent across different parameters. It is
noteworthy that, in most parameters, the two-stage training process
consistently outperformed SSL, although the degree of improvement remained
marginal (_p_ $>$ 0.05). While specifying the $certain$ value resulted in
enhancing GLP performance (evidenced by an increase from 0.49 to 0.57 for
linear interpolation), the correlation between $R^{2}$ and $certain$ was
determined to be weak based on Pearson correlation analysis (linear: -0.002,
PCHIP: 0.026, and barycentric: -0.040). This result indicated that $R^{2}$ and
$certain$ are not linear correlated. The detailed performances of $certain$
for each analysis can be found in Appendix 8. Based the best-performing linear
interpolation approach, we trained the GLP pretrain models by optimizing
$certain$ value for each parameters individually.

Table 3: Downstream task predictions |  AUROC |  Accuracy |  Sensitivity |  Specificity |  Precision |  F1
---|---|---|---|---|---|---
|  Original data (Mean Cohen’s Kappa = 0.366)
LGBM |  0.602 |  0.671 |  0.717 |  0.632 |  0.587 |  0.645
SVM |  0.313 |  0.718 |  0.603 |  0.836 |  0.804 |  0.667
LR |  0.543 |  0.494 |  0.673 |  0.398 |  0.425 |  0.500
KNN |  0.602 |  0.635 |  0.343 |  0.939 |  0.800 |  0.472
Avg. |  0.515 |  0.630 |  0.584 |  0.701 |  0.654 |  0.571
|  $Progress_{emb}$ (Mean Cohen’s Kappa = 0.307)
LGBM |  0.706 |  0.671 |  0.526 |  0.925 |  0.933 |  0.656
SVM |  0.601 |  0.435 |  0.450 |  0.511 |  0.231 |  0.290
LR |  0.536 |  0.494 |  0.362 |  0.657 |  0.510 |  0.357
KNN |  0.394 |  0.518 |  0.142 |  0.830 |  0.267 |  0.183
Avg. |  0.559 |  0.530* |  0.370* |  0.731 |  0.485 |  0.372*
|  $Progress_{out}$ (Mean Cohen’s Kappa = 0.941)
LGBM |  0.973 |  0.965 |  0.933 |  0.978 |  0.978 |  0.952
SVM |  0.907 |  0.882 |  0.767 |  0.969 |  0.933 |  0.822
LR |  0.863 |  0.871 |  0.708 |  1.000 |  1.000 |  0.824
KNN |  0.898 |  0.882 |  0.787 |  0.956 |  0.956 |  0.848
Avg. |  0.910** |  0.900** |  0.799** |  0.976** |  0.967** |  0.862**
LGBM: Light Gradient Boosting Machine; SVM: Support Vector Machine; LR:
Logistic Regression; KNN: K-Nearest Neighbors (KNN); AUROC: area under the
receiver operating characteristics curve; Avg.: average; *: Statistical
significant differences between $Progress_{emb}$ and original data; **:
Statistical significant differences between $Progress_{out}$ and original
data, as well as between $Progress_{out}$ and $Progress_{emb}$.

Figure 6: Alterations in distribution prior to and subsequent to GLP
processing. The values were inverse from normalization. $g$: time gap between
the input frame and prediction target.

Table 3 presents the result of the downstream task. After the processing by
GLP, $Progress_{out}$ exhibited a significant enhancement in classification
performance. On average, the measures attained an AUROC of 0.91, accuracy of
0.90, sensitivity of 0.80, specificity of 0.98, and F1 score of 0.86.
$Progress_{out}$ displayed a substantial superiority over the performance of
the original data (_p_ $<$ 0.01) and $Progress_{emb}$ (_p_ $<$ 0.01). The key
distinction between $Progress_{emb}$ and $Progress_{out}$ lies in the fact
that $Progress_{out}$ represents a condensed version that solely indicates the
laboratory values for the subsequent month, whereas $Progress_{emb}$ still
retains information on age, gender, $certain$, discrete value range encoding,
and laboratory values. This condensed version has a distillation effect, where
the $regressor$ was trained to provide a more valuable indication of future
trends based on these information.

Notably, LGBM emerged as the best-performing algorithm for both
$Progress_{emb}$ and $Progress_{out}$, while SVM demonstrated the highest
performance when utilizing the original data. The Kappa score demonstrates
that the agreement among classifiers increased from 0.37 to 0.94, indicating
that GLP alters the distribution of the original data and simplifies the
classification task, irrespective of the algorithm mechanism employed.

Fig.6 visually illustrates the changes in data distribution, using glucose AC
and LDL-c as exemplars. The figure depicts that prior to GLP processing (Fig.
6a), distinguishing between TVR and non-TVR cases was challenging. However,
after GLP processing, the non-TVR cases gradually converged towards a singular
point, while the TVR cases displayed a more scattered distribution (as
depicted in Fig. 6b to c).

##  4 Discussion

We have successfully engage in translational engineering by transferring the
progression of cardiovascular laboratory parameters from one patient group to
another, without confining to episodic observations. To the best of our
knowledge, this is the first study to apply the transfer of laboratory
progression between patient groups, demonstrating that disease progression can
be effectively transferred through deep neural network processing. The
generality of our findings is assured. The datasets were sourced from two
distinct medical institutions, signifying divergent patient populations,
personnel, operational protocols, and measurement equipment. This finding
opens up opportunities to leverage the trends observed in general cases for
developing data-driven applications targeting patient groups with limited data
availability.

Pretrained models capture the temporal dynamics and generate latent
representations that enhance predictive capabilities for other tasks. With the
widespread adoption of EHRs in modern hospitals, a large amount of data has
been collected from prevalent cases, such as patients with HTN and DM.
Conversely, specific cases, such as TVR occurance after PCI, remain limited in
number. Given that HTN and DM are known risk factors for TVR [32], we
successfully transferred the trend of laboratory progress observed in HTN
patients (who had yet to be diagnosed as diabetic) to predict the progression
of PCI patients.

Prior investigations [63] have highlighted that training deep neural network
models directly through gradient descent can yield randomly initialized models
that are less optimized. In contrast, commencing the training process with a
pretrained model enables the preservation and utilization of previously
acquired knowledge, thereby transforming our randomly initialized models into
exceptional pretrained feature extractors [13]. The practice of acquiring
knowledge from a more generalized domain and subsequently fine-tuning it to a
specific domain during pretraining has previously exhibited efficacy [30]. It
has been observed that representations acquired from supervised objectives
tend to be more domain-specific and possess limited transferability to out-of-
distribution domains [11, 13, 19]. Thus, modifying the model based on SSL
confers enhanced flexibility and facilitates the extrapolation of knowledge
across domains. This transformation enhanced our ability to infer patient
progress. Despite the heterogeneous characteristics exhibited by the two
patient cohorts, there existed intercorrelations among cardiometabolic
diseases.

SSL proves viable in bridging the gap caused by irregularity, absenteeism, and
sparsity in prolonged monitoring, enabling adequate prediction. Learning from
interpolated data in advance exhibits the capacity to enhance prediction
performance, however, the effects vary across interpolation methods. Linear
and PCHIP methodologies are designed to acquire a continuous function, while
barycentric interpolation is tailored to leverage the center of mass for
interpolation. The distribution pattern of laboratory progress resembles more
closely a continuous curve that extends over time, rather than being clustered
in discrete bundles. Our findings reveal that linear and PCHIP interpolation
yield more informative estimations, enhancing the performance of SSL. In the
case of LDL-c and LDL-c/HDL-c examinations, interpolation expands the
tolerance for periods of absence. Thus, our work implies that with sustainable
estimation, patient risks can be monitored with less frequent returns and
examinations (beyond the conventional 3-month interval), alleviating the
burdens of travel and medical expenses for patients.

The results indicate that without the support of GLP, the original data lacked
sufficient distinctiveness to enable substantial TVR predictions. Upon
processing the data with GLP, the classifier successfully achieved a
satisfactory separation. Non-TVR cases gradually converged towards a single
point, while TVR cases exhibited a more scattered pattern. These findings
align with clinical observations, which suggest that stable patients exhibit
less variation, whereas those with scattered observations face increased
risks. The distinctiveness is not constrained by algorithmic mechanisms.
However, it can be observed that the data were transformed from a hyperplane
cluster distribution (where SVM demonstrated highest performance in the
original distribution) to a more variable-specific representation (where LGBM
outperformed other methods) and facilitating the creation of tree-based rules.
Due to the limited size of the dataset, non-neural network algorithms were
employed for TVR prediction, as deep neural networks did not perform well in
this scenario.

The user scenario of GLP is depicted in Fig. 7. Following PCI treatment,
patients are required to undergo follow-up visits at the outpatient setting on
a 3-month basis. Clinicians assess patient risk to prevent TVR occurrence. GLP
identify patients at a higher risk, prompting the initiation of more advanced
examinations, such as treadmill tests, thallium scans, or coronary
computerized tomography (CT). Conversely, patients with a relatively lower
restenosis risk can also be separated, serving as a screening tool for more
precise event detection and examination resource allocation. In general, GLP
optimizes treatment strategies and improves patient prognosis while
maintaining simplicity and user-friendliness without relying on accumulating
comprehensive TVR predictors.

To broader the applications to other disease, an effective approach for
identifying the appropriate pretext and downstream tasks becomes
indispensable. Our findings suggested that through leveraging the
intercorrelations among diseases, such as comorbidity or shared risk factors,
represents a more proficient approach for selecting pretext and downstream
tasks. The intercorrelations imply underlying similarities and suggest the
possibility of translating disease progression between different patient
groups. This approach offers a more precise alternative to empirical trial-
and-error methods.

Figure 7: The user scenario of GLP adoption. Advanced examinations indicates
treadmill test, thallium scan, or coronary computerized tomography.

This study is subjected to certain limitations: While other approaches, such
as GAN, have demonstrated effectiveness in recovering the distribution of
absent observations, we opted for interpolation as it is considered a simpler
and less computationally expensive method that still achieves excellent
transfer performances. However, further exploration is required to determine
the necessity or indispensable advantages of integrating a more advanced
distribution generator; Our work is constrained by the unavailability of
genuine patient observations at the target event, which prevented us from
precisely reversing the exact output. Additionally, it is important to
acknowledge that, at present, the output of GLP corresponds to a
representation that cannot be straightforwardly reconstructed as real-world
values through simple inverse-normalization. To achieve accurate inverse
mapping, training an additional network or a decoder might be necessary to
effectively map the latent output to real-world values; Furthermore, it is
worth noting that this study exclusively focused on the analysis of numeric
laboratory results and its applicability is limited to other numeric
examinations. To make GLP a more comprehensive laboratory information
extractor, it is necessary to incorporate different types of laboratory
analyses, including categorical variables.

SSL also inherits certain limitations, such as the need for a larger amount of
training data, more training iterations, and computational expenses compared
to supervised training [15]. Additionally, deep neural networks employ a
substantial number of trainable parameters and layers, making the model
challenging to interpret [64, 19]. Thereby, SSL sacrificed interpretability
for accuracy. This consideration should be taken seriously when utilizing the
technology, as previous research has indicated that the level of trust in AI
systems impacts the outcome of system utilization [65, 66]. Apart from
interpretability, adopting AI system in to clinical workflow involved in
multidisciplinary integration, encompassing areas such as the user interface
design (research of Human-Computer Interactions) [67, 65], and factors that
influence the acceptance of technology [66]. Ensuring system usability and
reliability while resolving the disparities between proof-of-concepts and
real-life environments is crucial for the successful adoption of AI systems
into daily practice. This endeavor necessitates further collaboration to
facilitate the reform of healthcare.

##  5 Conclusion

Our research successfully translate the progression trends of cardiovascular
laboratory parameters between patient groups by capitalizing on the advantages
of SSL and pretrained models. This discovery paves the road for wider data-
driven applications in healthcare, and also functions as a screening tool for
more precise event detection and judicious allocation of examination
resources. Additionally, our study suggests that patients can benefits from
diminished frequency of visits and onerous examinations by implementing
sustainable estimation. To accomplish healthcare reform through the
utilization of AI systems, the key to success and optimal system utilization
lies in the multifaceted aspects involved multidisciplinary collaborations.

## Acknowledgment

This work was supported by the National Science and Technology Council,
Taiwan, NSTC 111-2628-E-A49-026-MY3, NSTC 112-2321-B-075-002- and Taipei
Veterans General Hospital under Grant V1118-031.

##  6 Onset definition for patients with hypertension and diabetes mellitus.

HTN ICD code |
---|---
|  ICD-9-CM |  codes starting with 401, 402, 403, 404, 405
|  ICD-10 |  codes starting with I10, I11, I12, I13, I15, I16
HTN Drug |
|  Candesartan, Bisoprolol, Captopril, Enalapril, Amlodipine, Enalapril,
Losartan Potassium, Carvedilol, Diltiazem HCL, Nifedipine, Felodipine,
Lercanidipine, Nicardipine HCL, Nimodipine, Sotalol HCL, Verapamil HCL,
Labetalol HCL, Fosinopril Sodium, Atenolol, Valsartan, Irbesartan, Metoprolol,
Clonidine, Hydralazine HCL, Minoxidil, Benzyl Hydrochlorothiazide, Indapamide,
Spironolactone, Pindolol, Nebivolol, Doxazosin SR, Eplerenone, Nicardipine,
Ramipril, Imidapril HCL, Telmisartan, Olmesartan, Azilsartan Medoxomil.
DM ICD code |
|  ICD-9-CM |  codes starting with 250
|  ICD-10 |  codes starting with E08, E09, E10, E11, E13
DM Drug |
|  Metformin
HTN: hypertension; DM: diabetes mellitus; ICD: International Classification of
Diseases (ICD); CM: clinical modification.

##  7 Discrete value thresholds of laboratory parameters

Item |  Encoding |  Value range
---|---|---
Chol/HDL-c |  0 |  $\leq 5$
1 |  $>5$
LDL-c |  0 |  $\leq 160$
1 |  $>160$
LDL-c/HDL-c |  0 |  $\leq 3.5$
1 |  $>3.5$
Glucose AC |  0 |  $\leq 100$
1 |  $100<$ value $\leq 125$
2 |  $<125$
WBC |  0 |  $<4$
1 |  $4\leq$ value $<9$
2 |  $\geq 9$
UA |  0 |  $\leq 3.4$
1 |  $3.4<$ value $\leq 7$
2 |  $<7$
Chol/HDL-c: ratio of total cholesterol and high-density lipoprotein
cholesterol; LDL-c: low-density lipoprotein cholesterol; LDL-c/HDL-c: ratio of
low-density and high-density lipoprotein cholesterol; Glucose AC: fasting
blood sugar; WBC: white blood cells; UA: Uric Acid.

##  8 $R^{2}$ values of each laboratory parameters based on linear and PCHIP
interpolation in two-stage training

c | Linear | PCHIP | Barycentric
---|---|---|---
mean |  $v_{CI}$ |  $v_{2stage}$ |  mean |  $v_{CI}$ |  $v_{2stage}$ |  mean |  $v_{CI}$ |  $v_{2stage}$
|  Chol/HDL-c
0 |  0.539 |  0.012 |  0.397 |  0.546 |  0.025 |  0.345 |  0.427 |  0.007 |  $-0.238$
1 |  0.565 |  0.024 |  0.368 |  0.552 |  0.058 |  0.343 |  0.486 |  0.018 |  $-0.177$
2 |  0.570 |  0.024 |  0.326 |  0.564 |  0.021 |  0.260 |  0.527 |  0.074 |  $-0.686$
3 |  0.586 |  0.043 |  0.348 |  0.575 |  0.054 |  0.309 |  0.490 |  0.025 |  $-0.302$
4 |  0.486 |  0.119 |  0.258 |  0.377 |  0.077 |  0.231 |  0.519 |  0.109 |  $-7.851$
5 |  0.602 |  0.057 |  0.281 |  0.586 |  0.039 |  0.228 |  0.295 |  0.094 |  $-5.464$
|  LDL-c
0 |  0.326 |  0.022 |  0.613 |  0.329 |  0.025 |  0.562 |  0.247 |  0.040 |  $-0.130$
1 |  0.348 |  0.034 |  0.598 |  0.338 |  0.018 |  0.568 |  0.257 |  0.040 |  0.182
2 |  0.326 |  0.221 |  0.618 |  0.344 |  0.019 |  0.472 |  0.299 |  0.018 |  $-0.240$
3 |  0.376 |  0.011 |  0.568 |  0.341 |  0.033 |  0.573 |  0.284 |  0.051 |  $-0.034$
4 |  0.207 |  0.349 |  0.272 |  0.213 |  0.119 |  0.382 |  $-0.475$ |  0.251 |  $-6.924$
5 |  0.234 |  0.095 |  0.402 |  0.311 |  0.070 |  0.526 |  0.157 |  0.164 |  $-4.875$
|  LDL-c/HDL-c
0 |  0.479 |  0.028 |  0.466 |  0.481 |  0.029 |  0.426 |  0.374 |  0.019 |  $-0.070$
1 |  0.505 |  0.074 |  0.446 |  0.503 |  0.033 |  0.407 |  0.417 |  0.014 |  0.050
2 |  0.509 |  0.043 |  0.399 |  0.521 |  0.019 |  0.313 |  0.444 |  0.020 |  $-0.271$
3 |  0.522 |  0.053 |  0.414 |  0.496 |  0.029 |  0.382 |  0.454 |  0.032 |  $-0.026$
4 |  0.488 |  0.413 |  $-0.077$ |  0.328 |  0.167 |  0.124 |  0.321 |  0.502 |  $-8.539$
5 |  0.297 |  0.122 |  0.433 |  0.513 |  0.022 |  0.272 |  0.290 |  0.106 |  $-3.942$
|  Glucose AC
0 |  0.480 |  0.040 |  0.426 |  0.480 |  0.053 |  0.392 |  0.360 |  0.014 |  $-1.987$
1 |  0.495 |  0.034 |  0.373 |  0.483 |  0.056 |  0.330 |  0.387 |  0.041 |  $-1.799$
2 |  0.546 |  0.096 |  0.262 |  0.508 |  0.090 |  0.268 |  0.451 |  0.085 |  $-2.099$
3 |  0.500 |  0.031 |  0.361 |  0.487 |  0.083 |  0.306 |  0.403 |  0.032 |  $-2.496$
4 |  0.666 |  0.319 |  0.075 |  0.602 |  0.343 |  0.184 |  0.516 |  0.121 |  $-4.839$
5 |  0.544 |  0.229 |  0.203 |  0.577 |  0.252 |  0.167 |  0.533 |  0.280 |  $-3.504$
|  WBC
0 |  0.664 |  0.024 |  0.245 |  0.657 |  0.022 |  0.226 |  0.564 |  0.027 |  $-8.366$
1 |  0.712 |  0.037 |  0.170 |  0.708 |  0.011 |  0.135 |  0.617 |  0.018 |  $-3.608$
2 |  0.744 |  0.022 |  0.106 |  0.758 |  0.022 |  0.060 |  0.701 |  0.013 |  $-2.309$
3 |  0.743 |  0.018 |  0.117 |  0.721 |  0.041 |  0.097 |  0.670 |  0.019 |  $-3.949$
4 |  0.774 |  0.019 |  0.058 |  0.761 |  0.012 |  0.051 |  0.716 |  0.026 |  $-7.218$
5 |  0.763 |  0.020 |  0.085 |  0.779 |  0.016 |  0.010 |  0.734 |  0.036 |  $-6.471$
|  UA
0 |  0.311 |  0.029 |  0.581 |  0.310 |  0.047 |  0.519 |  0.254 |  0.053 |  $-1.894$
1 |  0.333 |  0.038 |  0.539 |  0.325 |  0.047 |  0.472 |  0.256 |  0.079 |  $-1.313$
2 |  0.339 |  0.119 |  0.479 |  0.337 |  0.065 |  0.431 |  0.269 |  0.085 |  $-1.872$
3 |  0.355 |  0.040 |  0.521 |  0.328 |  0.033 |  0.415 |  0.257 |  0.058 |  $-2.296$
4 |  0.454 |  0.179 |  0.290 |  0.254 |  0.303 |  0.170 |  0.415 |  0.260 |  $-3.265$
5 |  0.272 |  0.231 |  0.235 |  0.373 |  0.089 |  0.433 |  0.283 |  0.149 |  $-2.886$
Avg. |  0.491 |  0.091 |  0.340 |  0.482 |  0.068 |  0.316 |  0.394 |  0.083 |  $-2.825$
$v_{CI}$: boundary range of the 95% confidence interval; $v_{2stage}$:
prediction variation between stage 1 and 2; c: $certain$. The bold figures are
the chosen best performed settings.

## References

  * [1] N. Sattar, J. M. Gill, and W. Alazawi, “Improving prevention strategies for cardiometabolic disease,” Nature medicine, vol. 26, no. 3, pp. 320–325, 2020.
  * [2] S. B. Hulley, S. R. Cummings, T. B. Newman, W. Browner, and D. Grady, “Designing cross-sectional and cohort studies,” Designing clinical research, vol. 4, pp. 85–96, 2013.
  * [3] J. Zhang, J. Gong, and L. Barnes, “HCNN: Heterogeneous convolutional neural networks for comorbid risk prediction with electronic health records,” in 2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), pp. 214–221, 2017\.
  * [4] Y. Cheng, F. Wang, P. Zhang, and J. Hu, “Risk prediction with electronic health records: A deep learning approach,” in Proceedings of the 2016 SIAM International Conference on Data Mining (SDM), pp. 432–440.
  * [5] S. Tang, P. Davarmanesh, Y. Song, D. Koutra, M. W. Sjoding, and J. Wiens, “Democratizing ehr analyses with fiddle: a flexible data-driven preprocessing pipeline for structured clinical data,” Journal of the American Medical Informatics Association, vol. 27, no. 12, pp. 1921–1934, 2020\.
  * [6] S. Wang, M. B. McDermott, G. Chauhan, M. Ghassemi, M. C. Hughes, and T. Naumann, “Mimic-extract: A data extraction, preprocessing, and representation pipeline for MIMIC-III,” in Proceedings of the ACM conference on health, inference, and learning, pp. 222–235, 2020.
  * [7] Y. Si, J. Du, Z. Li, X. Jiang, T. Miller, F. Wang, W. Jim Zheng, and K. Roberts, “Deep representation learning of patient data from electronic health records (EHR): A systematic review,” Journal of Biomedical Informatics, vol. 115, p. 103671, 2021.
  * [8] R. Miotto, F. Wang, S. Wang, X. Jiang, and J. T. Dudley, “Deep learning for healthcare: review, opportunities and challenges,” Briefings in bioinformatics, vol. 19, no. 6, pp. 1236–1246, 2018.
  * [9] A. Zaitcev, M. R. Eissa, Z. Hui, T. Good, J. Elliott, and M. Benaissa, “A deep neural network application for improved prediction of $\text{HbA}_{\text{1c}}$ in type 1 diabetes,” IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 10, pp. 2932–2941, 2020.
  * [10] H. H. Mao, “A survey on self-supervised pre-training for sequential transfer learning in neural networks,” 2020.
  * [11] M. Ragab, E. Eldele, Z. Chen, M. Wu, C.-K. Kwoh, and X. Li, “Self-supervised autoregressive domain adaptation for time series data,” IEEE Transactions on Neural Networks and Learning Systems, pp. 1–11, 2022.
  * [12] C. J. Reed, X. Yue, A. Nrusimha, S. Ebrahimi, V. Vijaykumar, R. Mao, B. Li, S. Zhang, D. Guillory, S. Metzger, K. Keutzer, and T. Darrell, “Self-supervised pretraining improves self-supervised pretraining,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 2584–2594, January 2022.
  * [13] X. Liu, F. Zhang, Z. Hou, L. Mian, Z. Wang, J. Zhang, and J. Tang, “Self-supervised learning: Generative or contrastive,” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 1, pp. 857–876, 2023.
  * [14] J. Yoon, Y. Zhang, J. Jordon, and M. van der Schaar, “Vime: Extending the success of self- and semi-supervised learning to tabular domain,” in Advances in Neural Information Processing Systems (H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, eds.), vol. 33, pp. 11033–11043, Curran Associates, Inc., 2020.
  * [15] D. Spathis, I. Perez-Pozuelo, L. Marques-Fernandez, and C. Mascolo, “Breaking away from labels: The promise of self-supervised machine learning in intelligent health,” Patterns, vol. 3, no. 2, p. 100410, 2022.
  * [16] D. Kiyasseh, T. Zhu, and D. A. Clifton, “Clocs: Contrastive learning of cardiac signals across space, time, and patients,” in Proceedings of the 38th International Conference on Machine Learning (M. Meila and T. Zhang, eds.), vol. 139 of Proceedings of Machine Learning Research, pp. 5606–5615, PMLR, 18–24 Jul 2021.
  * [17] R. Krishnan, P. Rajpurkar, and E. J. Topol, “Self-supervised learning in medicine and healthcare,” Nature Biomedical Engineering, vol. 6, no. 12, pp. 1346–1352, 2022.
  * [18] T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in International conference on machine learning, pp. 1597–1607, PMLR, 2020.
  * [19] L. Simone and D. Bacciu, “ECGAN: Self-supervised generative adversarial network for electrocardiography,” in Artificial Intelligence in Medicine, pp. 276–280, Springer Nature Switzerland, 2023.
  * [20] P. Lee, S. Bubeck, and J. Petro, “Benefits, limits, and risks of gpt-4 as an ai chatbot for medicine,” New England Journal of Medicine, vol. 388, no. 13, pp. 1233–1239, 2023.  PMID: 36988602.
  * [21] X. Zhang, Z. Zhao, T. Tsiligkaridis, and M. Zitnik, “Self-supervised contrastive pre-training for time series via time-frequency consistency,” in Advances in Neural Information Processing Systems (S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, eds.), vol. 35, pp. 3988–4003, Curran Associates, Inc., 2022.
  * [22] K. Wickstrøm, M. Kampffmeyer, K. Øyvind Mikalsen, and R. Jenssen, “Mixing up contrastive learning: Self-supervised representation learning for time series,” Pattern Recognition Letters, vol. 155, pp. 54–61, 2022.
  * [23] J. Ouyang, D. Mao, Z. Guo, S. Liu, D. Xu, and W. Wang, “Contrastive self-supervised learning for diabetic retinopathy early detection,” Medical & Biological Engineering & Computing, pp. 1–12, 2023.
  * [24] K. Weiss, T. M. Khoshgoftaar, and D. Wang, “A survey of transfer learning,” Journal of Big data, vol. 3, no. 1, pp. 1–40, 2016.
  * [25] C. I. Tang, I. Perez-Pozuelo, D. Spathis, S. Brage, N. Wareham, and C. Mascolo, “SelfHAR: Improving human activity recognition through self-training with unlabeled data,” Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies, vol. 5, no. 1, pp. 1–30, 2021.
  * [26] D. Spathis, I. Perez-Pozuelo, S. Brage, N. J. Wareham, and C. Mascolo, “Self-supervised transfer learning of physiological representations from free-living wearable data,” in Proceedings of the Conference on Health, Inference, and Learning, CHIL ’21, (New York, NY, USA), p. 69–78, Association for Computing Machinery, 2021.
  * [27] S. Tipirneni and C. K. Reddy, “Self-supervised transformer for sparse and irregularly sampled multivariate clinical time-series,” ACM Trans. Knowl. Discov. Data, vol. 16, jul 2022.
  * [28] A. Chowdhury, J. Rosenthal, J. Waring, and R. Umeton, “Applying self-supervised learning to medicine: Review of the state of the art and medical implementations,” Informatics, vol. 8, no. 3, 2021.
  * [29] Y. Liu, K. Gadepalli, M. Norouzi, G. E. Dahl, T. Kohlberger, A. Boyko, S. Venugopalan, A. Timofeev, P. Q. Nelson, G. S. Corrado, J. D. Hipp, L. Peng, and M. C. Stumpe, “Detecting cancer metastases on gigapixel pathology images,” 2017.
  * [30] J. Lee, W. Yoon, S. Kim, D. Kim, S. Kim, C. H. So, and J. Kang, “Biobert: a pre-trained biomedical language representation model for biomedical text mining,” Bioinformatics, vol. 36, no. 4, pp. 1234–1240, 2020.
  * [31] Y. Gu, R. Tinn, H. Cheng, M. Lucas, N. Usuyama, X. Liu, T. Naumann, J. Gao, and H. Poon, “Domain-specific language model pretraining for biomedical natural language processing,” ACM Trans. Comput. Healthcare, vol. 3, oct 2021.
  * [32] E. Braunwald, “Shattuck lecture—cardiovascular medicine at the turn of the millennium,” N Engl J Med, vol. 337, pp. 1360–1369, 1997.
  * [33] V. Fuster, “Lewis a. conner memorial lecture. mechanisms leading to myocardial infarction: insights from studies of vascular biology.,” Circulation, vol. 90, no. 4, pp. 2126–2146, 1994.
  * [34] G. W. Stone, “Percutaneous coronary intervention of chronic total occlusions: Conquering the final frontier,” 2018.
  * [35] G. Giustino, A. Colombo, A. Camaj, K. Yasumura, R. Mehran, G. W. Stone, A. Kini, and S. K. Sharma, “Coronary in-stent restenosis: Jacc state-of-the-art review,” Journal of the American College of Cardiology, vol. 80, no. 4, pp. 348–372, 2022.
  * [36] P. W. Serruys, M. J. Kutryk, and A. T. Ong, “Coronary-artery stents,” New England Journal of Medicine, vol. 354, no. 5, pp. 483–495, 2006.
  * [37] G. D. Dangas, B. E. Claessen, A. Caixeta, E. A. Sanidas, G. S. Mintz, and R. Mehran, “In-stent restenosis in the drug-eluting stent era,” Journal of the American College of Cardiology, vol. 56, no. 23, pp. 1897–1907, 2010.
  * [38] M. Nakano, F. Otsuka, K. Yahagi, K. Sakakura, R. Kutys, E. R. Ladich, A. V. Finn, F. D. Kolodgie, and R. Virmani, “Human autopsy study of drug-eluting stents restenosis: histomorphological predictors and neointimal characteristics,” European heart journal, vol. 34, no. 42, pp. 3304–3313, 2013.
  * [39] J. M. Stolker, D. J. Cohen, K. F. Kennedy, M. J. Pencina, J. B. Lindsey, L. Mauri, D. E. Cutlip, and N. S. Kleiman, “Repeat revascularization after contemporary percutaneous coronary intervention: an evaluation of staged, target lesion, and other unplanned revascularization procedures during the first year,” Circulation: Cardiovascular Interventions, vol. 5, no. 6, pp. 772–782, 2012.
  * [40] G. W. Stone, A. Maehara, A. J. Lansky, B. De Bruyne, E. Cristea, G. S. Mintz, R. Mehran, J. McPherson, N. Farhat, S. P. Marso, et al., “A prospective natural-history study of coronary atherosclerosis,” New England Journal of Medicine, vol. 364, no. 3, pp. 226–235, 2011.
  * [41] D. E. Cutlip, S. Windecker, R. Mehran, A. Boam, D. J. Cohen, G.-A. van Es, P. Gabriel Steg, M.-a. Morel, L. Mauri, P. Vranckx, et al., “Clinical end points in coronary stent trials: a case for standardized definitions,” Circulation, vol. 115, no. 17, pp. 2344–2351, 2007.
  * [42] X. Sun and T. Du, “Trends in cardiovascular risk factors among us men and women with and without diabetes, 1988–2014,” BMC Public Health, vol. 17, no. 1, pp. 1–13, 2017.
  * [43] P. M. Ridker, B. M. Everett, T. Thuren, J. G. MacFadyen, W. H. Chang, C. Ballantyne, F. Fonseca, J. Nicolau, W. Koenig, S. D. Anker, et al., “Antiinflammatory therapy with canakinumab for atherosclerotic disease,” New England journal of medicine, vol. 377, no. 12, pp. 1119–1131, 2017\.
  * [44] C. Kim, B.-K. Kim, S.-Y. Lee, D.-H. Shin, J.-S. Kim, Y.-G. Ko, D. Choi, Y. Jang, and M.-K. Hong, “Incidence, clinical presentation, and predictors of early neoatherosclerosis after drug-eluting stent implantation,” American heart journal, vol. 170, no. 3, pp. 591–597, 2015.
  * [45] A. Kastrati, A. Dibra, J. Mehilli, S. Mayer, S. Pinieck, J. Pache, J. Dirschinger, and A. Schömig, “Predictive factors of restenosis after coronary implantation of sirolimus-or paclitaxel-eluting stents,” Circulation, vol. 113, no. 19, pp. 2293–2300, 2006.
  * [46] S. Cassese, R. A. Byrne, T. Tada, S. Pinieck, M. Joner, T. Ibrahim, L. A. King, M. Fusaro, K.-L. Laugwitz, and A. Kastrati, “Incidence and predictors of restenosis after coronary stenting in 10 004 patients with surveillance angiography,” Heart, vol. 100, no. 2, pp. 153–159, 2014.
  * [47] R. W. Yeh, S.-L. T. Normand, R. E. Wolf, P. G. Jones, K. K. Ho, D. J. Cohen, D. E. Cutlip, L. Mauri, A. D. Kugelmass, A. P. Amin, et al., “Predicting the restenosis benefit of drug-eluting versus bare metal stents in percutaneous coronary intervention,” Circulation, vol. 124, no. 14, pp. 1557–1564, 2011.
  * [48] J. M. Stolker, D. J. Cohen, K. F. Kennedy, M. J. Pencina, S. V. Arnold, N. S. Kleiman, J. A. Spertus, E. Investigators, et al., “Combining clinical and angiographic variables for estimating risk of target lesion revascularization after drug eluting stent placement,” Cardiovascular Revascularization Medicine, vol. 18, no. 3, pp. 169–176, 2017.
  * [49] D. J. Gordon and B. M. Rifkind, “High-density lipoprotein—the clinical implications of recent studies,” New England Journal of Medicine, vol. 321, no. 19, pp. 1311–1316, 1989.
  * [50] J. B. Zalokar, J. L. Richard, and J. R. Claude, “Leukocyte count, smoking, and myocardial infarction,” New England Journal of Medicine, vol. 304, no. 8, pp. 465–468, 1981.
  * [51] C. Hage, A. Norhammar, L. Grip, K. Malmberg, N. Sarkar, B. Svane, and L. Rydén, “Glycaemic control and restenosis after percutaneous coronary interventions in patients with diabetes mellitus: a report from the insulin diabetes angioplasty study,” Diabetes and Vascular Disease Research, vol. 6, no. 2, pp. 71–79, 2009.
  * [52] C. Mozzini, D. Girelli, A. Setti, J. Croce, F. Stefanoni, A. Castagna, F. Pizzolo, S. Friso, O. Olivieri, and N. Martinelli, “Serum uric acid levels, but not rs7442295 polymorphism of scl2a9 gene, predict mortality in clinically stable coronary artery disease,” Current Problems in Cardiology, vol. 46, no. 5, p. 100798, 2021.
  * [53] E. Expert Panel on Detection et al., “Executive summary of the third report of the national cholesterol education program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III),” JAMA, vol. 285, no. 19, pp. 2486–2497, 2001.
  * [54] “scipy.interpolate.PchipInterpolator — SciPy v1.9.1 manual.” https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.PchipInterpolator.html.  Accessed date: 2022-09-19.
  * [55] J.-P. Berrut and L. N. Trefethen, “Barycentric lagrange interpolation,” SIAM review, vol. 46, no. 3, pp. 501–517, 2004.
  * [56] K. Hormann, “Barycentric interpolation,” in Approximation Theory XIV: San Antonio 2013, pp. 197–218, Springer, 2014.
  * [57] D. E. Cutlip, A. G. Chhabra, D. S. Baim, M. S. Chauhan, S. Marulkar, J. Massaro, A. Bakhai, D. J. Cohen, R. E. Kuntz, and K. K. Ho, “Beyond restenosis: five-year clinical outcomes from second-generation coronary stent trials,” Circulation, vol. 110, no. 10, pp. 1226–1230, 2004.
  * [58] S. Siami-Namini, N. Tavakoli, and A. S. Namin, “The performance of lstm and bilstm in forecasting time series,” in 2019 IEEE International Conference on Big Data (Big Data), pp. 3285–3292, 2019.
  * [59] “numpy.log1p - NumPy v1.23 manual.” https://numpy.org/doc/stable/reference/generated/numpy.log1p.html.  Accessed date: 2022-09-19.
  * [60] S. Kaufman, S. Rosset, C. Perlich, and O. Stitelman, “Leakage in data mining: Formulation, detection, and avoidance,” ACM Trans. Knowl. Discov. Data, vol. 6, dec 2012.
  * [61] R. Kohavi et al., “A study of cross-validation and bootstrap for accuracy estimation and model selection,” in Ijcai, vol. 14, pp. 1137–1145, Montreal, Canada, 1995.
  * [62] S.-C. Shao, Y.-Y. Chan, Y.-H. Kao Yang, S.-J. Lin, M.-J. Hung, R.-N. Chien, C.-C. Lai, and E. C.-C. Lai, “The Chang Gung Research Database—a multi-institutional electronic medical records database for real-world epidemiological studies in Taiwan,” Pharmacoepidemiology and drug safety, vol. 28, no. 5, pp. 593–600, 2019.
  * [63] P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, P.-A. Manzagol, and L. Bottou, “Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion.,” Journal of machine learning research, vol. 11, no. 12, 2010.
  * [64] S. Nazir, D. M. Dickson, and M. U. Akram, “Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks,” Computers in Biology and Medicine, vol. 156, p. 106668, 2023.
  * [65] F. M. Calisto, J. a. Fernandes, M. Morais, C. Santiago, J. a. M. Abrantes, N. Nunes, and J. C. Nascimento, “Assertiveness-based agent communication for a personalized medicine on medical imaging diagnosis,” in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, CHI ’23, (New York, NY, USA), Association for Computing Machinery, 2023.
  * [66] F. M. Calisto, N. Nunes, and J. C. Nascimento, “Modeling adoption of intelligent agents in medical imaging,” International Journal of Human-Computer Studies, vol. 168, p. 102922, 2022.
  * [67] F. M. Calisto, A. Ferreira, J. C. Nascimento, and D. Gonçalves, “Towards touch-based medical image diagnosis annotation,” in Proceedings of the 2017 ACM International Conference on Interactive Surfaces and Spaces, (New York, NY, USA), p. 390–395, Association for Computing Machinery, 2017.