Risk Assessment Model for Diabetic Cardiovascular Disease

Via Personality and Time-Aware LSTM Network

Dehua Chen

1,*

, Liping Zhang

1,*

, Ming Zuo

and Qiao Pan

School of Computer Science and Technology, Donghua University, Shanghai 201620, China

School of Medicine, Shanghai Jiao Tong University, Ruijin Hospital, Shanghai 200025, China

Keywords:

Diabetic Cardiovascular Disease, Risk Assessment, Individual Feature Interaction.

Abstract:

Diabetic cardiovascular disease is one of the leading causes of disease death in the diabetic population and its

prevention and treatment has become a major social challenge. It has attracted the attention of many scholars

and experts around the world, and a lot of research work has been done on it. Most of them use cox

proportional risk models to investigate the correlation between risk indicators and the risk of developing

cardiovascular disease based on statistical methods, which lack attention to the heterogeneity of individual

patient characteristics and disease contextual information. To fill this gap, we propose a new deep learning

model, the Personality and Time-Aware LSTM (PT-LSTM), which is based on individual characteristics and

time perception to assess the risk of developing cardiovascular disease in diabetes. The model is able to take

into account the characteristics of chronic metabolic disease in diabetes, using information from long-term

patient visits as input. The model uses the individual feature interaction layer to reweight the hidden

information of disease information learned in the T-LSTM unit, resulting in a more accurate representation

of disease information for the risk assessment task. We realistically evaluate our proposed model on this task

and the experimental results show that our proposed model exhibits better performance. Compared to the

baseline model, PT-LSTM achieves 93.49% AUROC on the dataset for this task, which is on average around

8.75% higher than the comparison model.

1 INTRODUCTION

Diabetes is a chronic metabolic disease that causes a

variety of serious health complications, including

heart disease, kidney failure and cardiovascular

disease (CVD), and has become one of the most

significant disease burdens in our country and

worldwide (Forbes, 2013). Death due to

cardiovascular diseases as a complication of diabetes

is one of the leading causes of death in this population

(Grøntved, 2011). Therefore, the search for an

effective diabetic cardiovascular disease risk

assessment method for early prevention and treatment

of the disease could greatly improve the survival rate

of diabetic patients.

Most of the existing studies have used statistically

relevant experimental analyses such as cox

proportional risk models, logistic regression tests or

simple machine learning to calculate the correlation

between risk indicators and CVD risk or disease risk

scores. For example, Domanski M J et al (Mjd, 2020)

used Kaplan-Meier method estimates to assess the

association between low-density lipoprotein (LDL-C)

and CVD risk. Most of these methods are based on

statistical correlation of risk characteristics with

disease, treating both the important disease context of

diabetes and important individual characteristics such

as patient gender as simple risk characteristics. As a

result, most of them lack attention to the heterogeneity

of individual patient characteristics and ignore the

important information carried by the disease context.

Based on the above issues, we propose a model for

assessing cardiovascular disease risk in diabetes based

on individual interaction and time perception, namely

the Personality and Time-Aware LSTM (PT-LSTM).

PT-LSTM takes into account the chronic metabolic

disease characteristics of diabetes and is inspired by

the TLSTM model proposed by Baytas I M et al

(Baytas, 2017), which is applied on top of the T-

LSTM to design an individual feature interaction layer

that uses individual features to correct the hidden

information of disease information obtained from

learning Time-aware LSTM units to obtain a more

accurate representation of disease information.

Finally, we use a fully connected layer to assess the

Chen, D., Zhang, L., Zuo, M. and Pan, Q.

Risk Assessment Model for Diabetic Cardiovascular Disease Via Personality and Time-Aware LSTM Networ k.

DOI: 10.5220/0012032600003633

In Proceedings of the 4th International Conference on Biotechnology and Biomedicine (ICBB 2022), pages 467-475

ISBN: 978-989-758-637-8

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

467

patient's current risk of developing cardiovascular

disease.

In summary, the main contributions of this paper

are as follows.

(1) Considering the chronic metabolic disease

characteristics of diabetes mellitus, we adopt the

modelling idea of T-LSTM. For the diabetic

cardiovascular disease risk assessment task, we regard

patients' long-term medical visit data as time-series

information as the input to the model.

(2) An individual feature interaction network was

designed to incorporate individual patient features into

the model learning, resulting in a more accurate

representation of disease information features.

To demonstrate the effectiveness and superiority

of our model, we evaluated and compared the model

with traditional machine learning methods (LR, RF

and GBDT) and deep learning methods (RNN, GRU

and LSTM) on this task. The experimental results

show that our proposed model performs better in real-

world tasks, outperforming the compared baseline

models in terms of metrics such as AUROC.

2 RELATED WORK

Cardiovascular disease, as the leading cause of death

worldwide, is an important public health issue (Yang,

2020) and its associated disease risk research has

been a hot issue over the years, attracting the attention

of many scholars and experts at home and abroad. For

example, Bode E D et al (Bode, 2021) studied the risk

factors for cardiovascular disease in US firefighters

by BMI category based on statistical methods using

the Wald test and logistic regression models.

D'Agostino RB Sr et al (D’Agostino Sr, 2008)

constructed a predictive model for cardiovascular

disease in Framingham, USA, based on the general

population. Elley CR et al (Elley, 2010) used a cox

proportional risk regression model to construct a New

Zealand diabetes cohort based on patients with type 2

diabetes, assessing multiple risk factors such as

glycated haemoglobin associated with cardiovascular

disease. Conroy R M et al (Conroy, 2003) used the

Weibull proportional risk model to develop a risk

scoring system for the clinical management of

cardiovascular risk in European clinical practice.

These risk prediction algorithms are typically

developed using multivariate regression models and

often assume that all these factors are linearly related

to cardiovascular disease prognosis, allowing existing

algorithms to typically exhibit modest predictive

performance (Alaa, 2019). This has led some scholars

to propose data-driven techniques based on machine

learning (ML) to improve the performance of risk

prediction. For example, Mohan S et al (Mohan,

2019) combined random forest (RF) and linear

methods (LM) to propose a hybrid random forest

(HRFLM) heart disease prediction model with linear

models for improving the accuracy of predicting

cardiovascular disease. Dinh A et al (Dinh, 2019)

used multiple supervised learning models to classify

high-risk patients to obtain better performance than a

single algorithm.

All of these efforts have contributed to the study

of cardiovascular disease risk in diabetes. But these

models treat all relevant factors as the same, lack

attention to the clinical significance of individual

patient characteristics, and ignore important

information carried by the disease context. Such as

patient age, gender and the disease context of

diabetes. Patients with diabetes are at greater risk of

developing cardiovascular disease and the correlation

cannot be ignored (Einarson, 2018; Strain, 2018).

Therefore, it is important to further explore and

exploit diabetes information for cardiovascular

disease risk prediction tasks.

3 PT-LSTM METHOD

3.1 Overview

As shown in Fig.1, the PT-LSTM model is a three-

stage architecture consisting of three components: (1)

a feature learning module based on Time-aware

LSTM; (2) an interaction module of individual

characteristics based on attention; (3) a prediction

module with Fully Connected Network (FNN) for

disease risk assessment.

The PT-LSTM model uses the visit records and

visit time intervals in patients' EHR information as

inputs to the T-LSTM module to obtain the hidden

state ℎ



and 𝑐



at the first moment. In the attention-

based individual feature interaction module, the

observation window size is set to K, and the sequence

of disease hidden feature information

ℎ

:

(ℎ

:

=[ℎ



,...,ℎ



,ℎ



]) output in

the previous stage is used as input to the module

together with the individual patient features 𝑞. The

module outputs the reweighted weights β (β =

[β



,...,β



,β



]) of ℎ

:

based on the

interaction of the individual patient features 𝑞 with

the temporal hidden information ℎ

:

. Finally, the

disease risk information 𝑢 is obtained based on the

corrected temporal hidden information ℎ



:

and

individual characteristics 𝑞 to predict the risk of

disease occurrence 𝑦.

ICBB 2022 - International Conference on Biotechnology and Biomedicine

468

Figure 1: Overall architecture of Personality and Time-aware LSTM model

3.2

Time-Aware LSTM Module

T-LSTM is proposed based on the architecture of

LSTM, which merges runtime information into the

standard LSTM architecture and is able to focus on

the information dependencies between two adjacent

visit records (e.g. 𝑣



and 𝑣



) to capture the

temporal dynamics of sequential data with temporal

irregularities. Therefore, in order to capture long-term

information in patient medical data, in this paper we

use a Time-aware LSTM module to process the

temporal medical features in patient data, as shown in

Fig.2, which is computed as follows.

𝐶





= 𝑡𝑎𝑛ℎ(𝑊



𝐶



𝑏



) 𝐶







=𝐶





∗𝑔(∆



) (New short term memory)

𝐶





=𝐶



𝐶





𝐶





=𝐶





𝐶







(New previous memory)

𝑓



=𝜎(𝑊



𝑣



𝑈



ℎ



𝑏



) 𝑖



=𝜎(𝑊



𝑣



𝑈



ℎ



𝑏



)

𝑜



=𝜎(𝑊



𝑣



𝑈



ℎ



𝑏



) (Gate cell calculation)

𝐶



= 𝑡𝑎𝑛ℎ(𝑊



𝑣



𝑈



ℎ



𝑏



) 𝐶



=𝑓



∗𝐶





 𝑖



∗𝐶



(Current memory)

ℎ



=𝑜



∗𝑡𝑎𝑛ℎ( 𝐶



) (Current hidden state)

Here, 𝑣



represents the current input, ℎ



and

ℎ



are the hidden states of the previous and current

steps respectively. 𝐶



and 𝐶



are the unit

memory of the previous and current steps

respectively. 𝐶





represents the short-term

memory, 𝐶





is the short-term memory after

adjustment, 𝐶





represents the long-term memory.

Similarly, as with standard LSTM units, 𝐶



i s t h e

current candidate memory, 𝑓



, 𝑖



an d 𝑜



are the

input, forget and output gates respectively. In

addition, 𝑊 , 𝑈 and 𝑏 are the network parameters

to be trained, ∆



is the access interval between 𝑣



and 𝑣



, and 𝑔() is a heuristic decay function based

on the value ∆



, i.e. the larger the value of ∆



, the

smaller the effect on short-term memory.

Risk Assessment Model for Diabetic Cardiovascular Disease Via Personality and Time-Aware LSTM Network

469

Figure 2: The structure of Time-aware LSTM cell.

3.3

Attention-Based Feature

Interaction Module

In order to obtain more accurate information about

the characteristics that can represent the patient's

current risk of disease occurrence, we developed an

attention-based individual factor interaction layer

applied on the Time-aware LSTM cell, as shown in

Fig.3 (a), whose specific process can be represented

as the following three stages.

(1) Individual Feature Representation Layer

First, we count the discrete number of discrete

individual features as the word table size, and

according to the size of the number of possible values

of discrete features, we set the word vector dimension

size to 𝑁



. Subsequently, the discrete individual

features [𝑑



,...,𝑑





] are input to the embedding

layer, and the embedding vector [𝑒



,...,𝑒





],𝑒



∈

ℝ





of individual features is obtained based on

Word2Vec. Then, the matrix representation 𝑞 (𝑞∈

ℝ

(



∗



)

) of individual features is obtained by

vector stitching through the concat layer, and then

multiplied with the parameter matrix 𝑊



(𝑊



∈

ℝ

(



∗



)



) to obtain the latest representation

𝑞(𝑞 ∈ ℝ





) of individual features by matrix

variation.

(2) Activation Unit

The individual feature 𝑞 and the hidden state

sequence ℎ

:

are used as the input of this layer,

and the outer product 𝑝 of the two features is

calculated, which is then concatenated with these two

features to obtain the new feature representation. The

attention weights β are obtained by multi fully

connected network and linear layer. Here is an

example of the calculation process for a single ℎ



with 𝑞, as shown in Fig.3 (b), and the formula is

expressed as follows.

𝑝=𝑞∗ℎ



𝑅



=𝑅𝑒𝐿𝑢𝑊



(𝑞 ⊕ 𝑝⨁ℎ



)𝑏





𝑅



=𝑅𝑒𝐿𝑢(𝑊



𝑅



𝑏



) (1)

𝛽



=𝑆𝑜𝑓𝑡𝑀𝑎𝑥(𝑊



𝑅



𝑏



)

(3) Feature Interaction Layer

Based on the attention weights in the previous

layer, the modified disease hidden information

ℎ



:

is obtained, which is input to the SUM

Pooling layer for summation according to the 1st

dimension, and then concatenated with the individual

feature information to obtain the final disease

information feature representation 𝑢.

𝑢=𝑞 ⨁ 𝛽







ℎ



(2)

ICBB 2022 - International Conference on Biotechnology and Biomedicine

470

(a) Overall structure (b) The process of the Activation Unit

Figure 3: The construction of the Individual Factors Interactive Attention Layer.

3.4

Disease Risk Assessment Module

The disease risk assessment module takes the output

𝑢 from the previous stage as input and obtains a

binary label indicating the patient's current risk of

developing cardiovascular disease via a Fully

Connected Network. In addition, we choose the cross-

entropy function to calculate the loss, which is

calculated as follows.

𝑦=𝜎(𝑊



𝑢𝑏



)

ℒ(𝑦, 𝑦) = (𝑦log(y)  (1  𝑦)𝑙𝑜𝑔(1

 𝑦) (3)

Where 𝑊



(𝑊



∈ℝ





) is a network parameter,

𝑦 represents the true value of the patient's risk of

developing cardiovascular disease, and 𝑦 is the

output value of the model's disease risk assessment

function.

EXPERIMENT

4.1

Dataset Description

The study was approved by the Ethics Committee of

Ruijin Hospital and written informed consent was

obtained from each participating patient in

accordance with the Declaration of Helsinki. Patient

information is shown in Table 1.

Table 1: Details of Patient Information.

Statistic Value

DataSet

# patients 33048

# visit 61646

ositive label 12680

# ne

ative label 20368

% male 60.21%

Our dataset was selected from biochemical index

data of diabetic patients in Shanghai Ruijin Hospital

from August 1, 2009 to July 30, 2021, with a total of

33,048 patients and 61,646 visit records, including

19,899 men and 13,149 women. Combining domestic

and international literature and clinical

recommendations, we selected high-density

lipoprotein, low-density lipoprotein, cholesterol,

glycated hemoglobin, two-hour glucose and

triglycerides as inputs in terms of medical

characteristics. Also, for individual patient

characteristics, we selected patient gender, age and

history of the remaining four common complications

of diabetes (here, diabetic foot disease, diabetic

nephropathy, diabetic peripheral neuropathy and

diabetic eye disease).

4.2

Experiment Setting

We implemented our proposed baseline and target

models on tensorflow 2.2.0 and scikit-learn 1.0.2, and

trained them using the Adam optimizer. Through

Risk Assessment Model for Diabetic Cardiovascular Disease Via Personality and Time-Aware LSTM Network

471

parameter tuning, we set the learning rate to 0.001,

the dimensionality of the individual feature

embedding vector used in the deep learning baseline

and PT-LSTM models to 64, and the dimensionality

of the hidden vector to 128. In addition, we randomly

divided the dataset into ten sets, and all experimental

results were averaged by ten cross-validations, using

seven training sets, one validation set, and two testing

sets each time. Finally, we compared the performance

of all methods using four metrics: the area under the

receiver operating characteristic (AUROC) curves,

Accuracy, Recall and F1-Score in the test set as

measures.

To validate the effectiveness of our proposed

model, we evaluated our proposed PT-LSTM model

on different baseline models, including three

traditional machine learning methods, LR, RF, and

GBDT, and four deep learning methods, RNN, GRU,

LSTM, and T-LSTM. Among them, in order to

demonstrate the availability of individual feature

interaction, we also implemented three versions of

PT-LSTM and LSTM, namely PT-LSTM_Metabo,

PT-LSTM_Add, PT-LSTM_Concat, LSTM_Metabo,

LSTM_Add, and LSTM_Concat, respectively.

Notably, there are many advanced clinical prediction

models that use attentional mechanisms to extract

long-term dependencies in patients' historical visits

(Kamal, 2020; Lee, 2018), and they are orthogonal to

our contribution. We focus on taking into account the

heterogeneity of individual patient factors into the

model, and our model PT-LSTM can be easily

combined with attentional mechanisms.

4.3

Comparison Methods

To obtain the best performance of the model, all

models used in our experiments were involved in

parameter tuning. In this subsection, the PT-LSTM

model is used as an example to discuss and compare

the different effects of the number of patient medical

visits T and the observation window size K of the

individual-specific interaction layer on the model

performance.

4.3.1 Comparison of Parameter Selection

(1) Parameter Selection of T

Diabetic disease is a chronic metabolic disease, and

to accurately assess the risk of cardiovascular disease

in diabetic patients, it is important to effectively

follow up and learn the long-term health status of

patients. Setting K = 1, an experimental comparison

of our proposed PT-LSTM model regarding the

number of patient medical visits T was conducted.

As shown in Fig.4, the experimental results show

that each assessment index of the model improves as

T increases. Thus, we believe that tracking and

learning information about patients' long-term visits

can effectively improve the accuracy of patients'

cardiovascular disease risk assessment. We consider

that this is brought about by diabetes itself as a

chronic metabolic disease. Therefore, we should

collect as much information on patient visits as the

amount of data allows as a way to improve the

accuracy of the disease risk assessment task. In

addition, we observed that the model metrics reached

their best and started to stabilize when T was greater

than equal to 5. In order to reduce the impact of too

small a data volume on other model comparison

experiments later, we selected T of 5 as the parameter

for our later experiments.

Figure 4: Parameter selection of T.

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472

(2) Parameter Selection of K

Here, we set T=5 and discuss the influence and role

of the parameter K in the individual feature

interaction module on the use of the model. The

experimental results are shown in Fig.5. When K=2,

the model achieves the best performance, and when it

is larger than 2, the model performance decreases,

which is due to the long-term dependency of

information that has been modeled and learned in the

PT-LSTM. When T = 5, the observation window K is

set too large, e.g. K is greater than 2, which can lead

to the model focusing excessively on the repetitive

and redundant part of the feature information, thus

reducing the performance of the model. This also

confirms the advantage of our LSTM unit in learning

long-term dependence of information from a certain

perspective.

Figure 5: Parameter selection of K.

4.3.2 Comparison of Individual Feature

Fusion Methods

From the experimental results in Table 2, we can find

that the traditional LSTM model and our proposed

model can also have relatively good results with only

metabolic metrics, and their AUROC metrics can

reach 85.87% and 88.66%, respectively.

Subsequently, we added individual feature learning to

the models using the traditional feature fusion

methods concat and add, and both models showed

significant improvements in various metrics such as

AUROC, accuracy and F1-Score. This reflects the

importance of individual feature learning in disease

risk tasks. When we employ the interactive fusion

method of individual features based on attention

mechanism on LSTM and T-LSTM (i.e., LSTM_At

and PT-LSTM), the accuracy and other evaluation

metrics are significantly better than other models and

fusion methods, which can reach 91.02% and

94.09%, respectively.

The experimental results demonstrate the

effectiveness of our proposed individual feature

interaction network and support the superiority of our

model. In addition, comparing the LSTM and PT-

LSTM models and their respective improved models,

we can also find that effectively focusing on the

medical information carried by the irregularity of visit

time in medical data is of great significance and value

for our assessment of disease risk.

Table 2: Comparison of feature fusion methods.

Model

Feature Fusion

Evaluation Index

Metabo Indifac Interfus Accuracy F1-Score AUROC Recall

LSTM_Metabo √

0.8747 0.8359 0.8587 0.7258

LSTM

√

0.9007 0.8793 0.8923 0.8226

LSTM_Concat

√

0.8913 0.8678 0.8827 0.8118

LSTM

√

0.9102 0.8889 0.9002 0.8172

PT-LSTM_Metabo √

0.8936 0.8725 0.8866 0.8280

PT-LSTM

√

0.9291 0.9128 0.9199 0.8441

PT-LSTM_Concat

√

0.9314 0.9169 0.9238 0.8602

PT-LSTM √ √ √

0.9409 0.9291 0.9345 0.8817

Risk Assessment Model for Diabetic Cardiovascular Disease Via Personality and Time-Aware LSTM Network

473

“Metabo” here means that the model only uses

patient visit data as input data. “Concat” and “Add”

represent the fusion mode of medical characteristic

information and individual factors.

Here, we defined the patient visit data as “Metabo”,

individual factor as “Indifac”, and individual

characteristic interaction as “Interfus”.

4.3.3 Comparison of Different Producttion

Models

To further validate the superiority of our proposed

model, we evaluated our proposed PT-LSTM model

on different baseline models. The experimental

results are shown in Table 3, where the disease risk

assessment task almost always performs worse than

the deep learning model on machine learning. We

consider that it is because the machine learning model

loses the temporal information of medical visits and

the information of individual patient characteristics.

The T-LSTM outperforms the LSTM model,

demonstrating the importance of irregular visit timing

information in patient medical data in our diabetic

cardiovascular disease risk assessment task. It should

be noted that the individual feature fusion methods

used here for both the LSTM and T-LSTM models

are the ones they performed better in the previous

section, and in this case, our proposed model PT-

LSTM also shows significant advantages.

Table 3: Comparison of different models.

Model Accuracy F1-Score Recall AUROC

Baseline

GBDT

0.7626

0.7857

0.7899

0.7483

0.7839

0.7863

0.721

0.794

0.7897

0.7617

0.7859

0.7899

RNN 0.8960 0.8736 0.8172 0.8875

GRU 0.8960 0.8771 0.8441 0.8904

LSTM

T-LSTM

0.9007

0.9214

0.8793

0.9069

0.8226

0.8602

0.8923

0.9138

Proposed PT-LSTM 0.9409 0.9294 0.8852 0.9349

In summary, we have experimentally analyzed

and compared each important module of the model

and its overall performance. In addition, we compared

the parameter selection of the training model and

selected the optimal hyper-parameters. The

experimental results of the comparison with the

baseline model provide evidence for the effectiveness

and superiority of our proposed model.

5 CONCLUSION

In this study, we propose a new deep learning model

(PT-LSTM), for the task of assessing the risk of

developing cardiovascular complications in the

context of diabetes. Our can model is divided into

three phases. In the first stage, patient visit records

and visit intervals are used as input, and a time-aware

LSTM module is employed to learn disease

information carried by temporal data from patient

medical visits. In the second stage, individual patient

factors are interacted with the disease information

features obtained in the previous stage to obtain a

more comprehensive and accurate representation of

disease risk features. In the third stage, a fully

connected layer is used for our final disease risk

assessment. The experimental results show that our

model, based on the design of individual feature

interaction fusion, can learn patient information better

and make it consistently better than the base model.

Our model also shows better performance in this task

compared to other models.

Our proposed model effectively addresses the

problem of personalised assisted diagnosis in the

diabetic cardiovascular disease risk assessment task.

In clinical practice, we hope that our model can help

physicians identify patients at greater risk of diabetic

cardiovascular disease in order to prevent or delay the

onset of adverse outcomes. In the future, our model

needs to be further validated on a larger scale for its

adaptability and effectiveness in cross-hospital and

cross-disease problems to better advance the

application of Artificial Intelligence models in the

field of diabetic complication risk prediction.

ACKNOWLEDGMENTS

This work was supported by the National Key R&D

Program of China under Grant 2019YFE0190500.

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474

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