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

Shuai Niu (), Jing Ma (), Qing Yin (), Zhihua Wang (), Liang Bai () and Xian Yang ()
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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

Information Systems Frontiers, 2025, vol. 27, issue 2, No 2, 409-427

Abstract: 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.

Keywords: Longitudinal electronic health records; Artificial intelligence; Deep state-space models; Clinical decision support (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10796-024-10513-x

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