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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Qing Yin & Linda Zhong & Yunya Song & Liang Bai & Zhihua Wang & Chen Li & Yida Xu & Xian Yang, 2025. "A decision support system in precision medicine: contrastive multimodal learning for patient stratification," Annals of Operations Research, Springer, vol. 348(1), pages 579-607, May.
    • Handle: RePEc:spr:annopr:v:348:y:2025:i:1:d:10.1007_s10479-023-05545-6
    DOI: 10.1007/s10479-023-05545-6
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    References listed on IDEAS

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    1. David M. Blei & Alp Kucukelbir & Jon D. McAuliffe, 2017. "Variational Inference: A Review for Statisticians," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 859-877, April.
    2. Zeineb Affes & Rania Hentati-Kaffel, 2019. "Forecast bankruptcy using a blend of clustering and MARS model: case of US banks," Annals of Operations Research, Springer, vol. 281(1), pages 27-64, October.
    3. Zeineb Affes & Rania Hentati-Kaffel, 2019. "Forecast bankruptcy using a blend of clustering and MARS model: case of US banks," Post-Print hal-03045877, HAL.
    4. Zeineb Affes & Rania Hentati-Kaffel, 2019. "Forecast bankruptcy using a blend of clustering and MARS model: case of US banks," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03045877, HAL.
    5. Philippe Jardin, 2021. "Forecasting bankruptcy using biclustering and neural network-based ensembles," Annals of Operations Research, Springer, vol. 299(1), pages 531-566, April.
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