TY - JOUR
T1 - Modelling Patient Longitudinal Data for Clinical Decision Support
T2 - A Case Study on Emerging AI Healthcare Technologies
AU - Niu, Shuai
AU - Ma, Jing
AU - Yin, Qing
AU - Wang, Zhihua
AU - Bai, Liang
AU - Yang, Xian
N1 - This work is supported by the National Key Research and Development Program of China (No. 2021ZD0113303), and the National Natural Science Foundation of China (Nos. 62022052, 62276159).
Publisher Copyright:
© The Author(s) 2024.
PY - 2024/7/18
Y1 - 2024/7/18
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Clinical decision support
KW - Deep state-space models
KW - Longitudinal electronic health records
UR - http://www.scopus.com/inward/record.url?scp=85198951576&partnerID=8YFLogxK
U2 - 10.1007/s10796-024-10513-x
DO - 10.1007/s10796-024-10513-x
M3 - Journal article
AN - SCOPUS:85198951576
SN - 1387-3326
JO - Information Systems Frontiers
JF - Information Systems Frontiers
ER -
