A decision support system in precision medicine: contrastive multimodal learning for patient stratification

Qing Yin (), Linda Zhong (), Yunya Song (), Liang Bai (), Zhihua Wang (), Chen Li (), Yida Xu () and Xian Yang ()
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Qing Yin: University of Manchester
Linda Zhong: Nanyang Technological University
Yunya Song: Hong Kong Baptist University
Liang Bai: Shanxi University
Zhihua Wang: China Shanghai Institute for Advanced Study of Zhejiang University
Chen Li: Huazhong University of Science and Technology
Yida Xu: Hong Kong Baptist University
Xian Yang: University of Manchester

Annals of Operations Research, 2025, vol. 348, issue 1, No 24, 579-607

Abstract: Abstract Precision medicine aims to provide personalized healthcare for patients by stratifying them into subgroups based on their health conditions, enabling the development of tailored medical management. Various decision support systems (DSSs) are increasingly developed in this field, where the performance is limited to their capability of handling big amounts of heterogeneous and high-dimensional electronic health records (EHRs). In this paper, we focus on developing a deep learning model for patient stratification that can identify and explain patient subgroups from multimodal EHRs. The primary challenge is to effectively align and unify heterogeneous information from various modalities, which includes both unstructured and structured data. Here, we develop a Contrastive Multimodal learning model for EHR (ConMEHR) based on topic modelling. In ConMEHR, modality-level and topic-level contrastive learning (CL) mechanisms are adopted to obtain a unified representation space and diversify patient subgroups, respectively. The performance of ConMEHR will be evaluated on two real-world EHR datasets and the results show that our model outperforms other baseline methods.

Keywords: Modelling unstructured and structured patient data; Application of EHRs in precision medicine; Deep learning model for patient stratification; Multimodal contrastive learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10479-023-05545-6

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