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

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

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  • NETL
  • NSTL
  • Springer Nature
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.

Longitudinal electronic health recordsArtificial intelligenceDeep state-space modelsClinical decision support

Shuai Niu、Jing Ma、Qing Yin、Zhihua Wang、Liang Bai、Xian Yang

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Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China

Alliance Manchester Business School, The University of Manchester, Oxford Road, Manchester M13 9PL, UK

Shanghai Institute for Advanced Study, Zhejiang University, Dangui Road, Shang Hai 310023, Zhe Jiang, China

Computer and Information Technology School, Shanxi University, Wu Cheng Road, Taiyuan 030006, Shan Xi, China

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2025

10.1007/s10796-024-10513-x

Information systems frontiers

Information systems frontiers

ISSN：1387-3326
年,卷(期)：2025.27(2)
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