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This paper is in the following e-collection/theme issue:

Early Results from COVID-19 Studies (459) Methods and Instruments in Medical Informatics (420) Design and Usability of Clinical Software and EHRs (469) Machine Learning (3230)

Published on in Vol 9 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/65585, first published .
Healthcare workers in PPE care for elderly patient on oxygen in hospital

Explainable Machine Learning Framework for Dynamic Monitoring of Disease Prognostic Risk: Retrospective Cohort Study

Explainable Machine Learning Framework for Dynamic Monitoring of Disease Prognostic Risk: Retrospective Cohort Study

Authors of this article:

Tetsuo Ishikawa1, 2, 3, 4, 5, 6 Author Orcid Image ;   Masahiro Shinoda4 Author Orcid Image ;   Megumi Oya1, 3, 4, 6 Author Orcid Image ;   Koichi Ashizaki1, 4 Author Orcid Image ;   Shinichiro Ota4 Author Orcid Image ;   Kenichi Kamachi4 Author Orcid Image ;   Kazuhiro Sakurada1, 2 Author Orcid Image ;   Eiryo Kawakami1, 3, 4, 6 Author Orcid Image ;   Masaharu Shinkai4 Author Orcid Image
Article Authors Cited by (3) Tweetations (2) Metrics

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  2. Xiong J, Liu L, Chen Z, Cai W, Luo J, Liu S, Qin J, Chen F, Li X, Qiu Z, Shen Y. Prediction of Prolonged Length of Hospital Stay in Patients with Acute Exacerbations of Chronic Obstructive Pulmonary Disease: An Interpretable Machine Learning Tool. Journal of Clinical Medicine 2026;15(15):6127 View
  3. Wang Y, Li F, Yan T, Wu Y, Qi R, Zhang J, Wang Y, Sun Z, Zhang Y, Chen Y. Interpretable machine learning identifies immune-inflammatory and immunothrombotic biomarkers for myocardial injury and mortality risk stratification in severe pneumonia with diverse infectious etiologies. Frontiers in Cellular and Infection Microbiology 2026;16 View