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Modelling Patient Longitudinal Data for Clinical Decision Support: A Case Study on Emerging AI Healthcare Technologies

Author

Listed:
  • Shuai Niu

    (Hong Kong Baptist University)

  • Jing Ma

    (Hong Kong Baptist University)

  • Qing Yin

    (The University of Manchester)

  • Zhihua Wang

    (Zhejiang University)

  • Liang Bai

    (Shanxi University)

  • Xian Yang

    (The University of Manchester)

Abstract

The COVID-19 pandemic has highlighted the critical need for advanced technology in healthcare. Clinical Decision Support Systems (CDSS) utilizing Artificial Intelligence (AI) have emerged as one of the most promising technologies for improving patient outcomes. This study’s focus on developing a deep state-space model (DSSM) is of utmost importance, as it addresses the current limitations of AI predictive models in handling high-dimensional and longitudinal electronic health records (EHRs). The DSSM’s ability to capture time-varying information from unstructured medical notes, combined with label-dependent attention for interpretability, will allow for more accurate risk prediction for patients. As we move into a post-COVID-19 era, the importance of CDSS in precision medicine cannot be ignored. This study’s contribution to the development of DSSM for unstructured medical notes has the potential to greatly improve patient care and outcomes in the future.

Suggested Citation

  • Shuai Niu & Jing Ma & Qing Yin & Zhihua Wang & Liang Bai & Xian Yang, 2025. "Modelling Patient Longitudinal Data for Clinical Decision Support: A Case Study on Emerging AI Healthcare Technologies," Information Systems Frontiers, Springer, vol. 27(2), pages 409-427, April.
    • Handle: RePEc:spr:infosf:v:27:y:2025:i:2:d:10.1007_s10796-024-10513-x
    DOI: 10.1007/s10796-024-10513-x
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