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

Artificial Intelligence (1676) Machine Learning (1694) Clinical Information and Decision Making (1612) Clinical Informatics (1142)

Published on in Vol 24, No 6 (2022): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34295, first published .
Machine Learning–Based Prediction Models for Different Clinical Risks in Different Hospitals: Evaluation of Live Performance

Machine Learning–Based Prediction Models for Different Clinical Risks in Different Hospitals: Evaluation of Live Performance

Machine Learning–Based Prediction Models for Different Clinical Risks in Different Hospitals: Evaluation of Live Performance

Authors of this article:

Hong Sun1 Author Orcid Image ;   Kristof Depraetere1 Author Orcid Image ;   Laurent Meesseman1 Author Orcid Image ;   Patricia Cabanillas Silva1 Author Orcid Image ;   Ralph Szymanowsky1 Author Orcid Image ;   Janis Fliegenschmidt2 Author Orcid Image ;   Nikolai Hulde2 Author Orcid Image ;   Vera von Dossow2 Author Orcid Image ;   Martijn Vanbiervliet1 Author Orcid Image ;   Jos De Baerdemaeker1 Author Orcid Image ;   Diana M Roccaro-Waldmeyer1 Author Orcid Image ;   Jörg Stieg1 Author Orcid Image ;   Manuel Domínguez Hidalgo1 Author Orcid Image ;   Fried-Michael Dahlweid1 Author Orcid Image
Article Authors Cited by (20) Tweetations (9) Metrics

Journals

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  12. Liou L, Scott E, Parchure P, Ouyang Y, Egorova N, Freeman R, Hofer I, Nadkarni G, Timsina P, Kia A, Levin M. Assessing calibration and bias of a deployed machine learning malnutrition prediction model within a large healthcare system. npj Digital Medicine 2024;7(1) View
  13. Cabanillas Silva P, Sun H, Rodriguez-Brazzarola P, Rezk M, Zhang X, Fliegenschmidt J, Hulde N, von Dossow V, Meesseman L, Depraetere K, Szymanowsky R, Stieg J, Dahlweid F. Evaluating gender bias in ML-based clinical risk prediction models: A study on multiple use cases at different hospitals. Journal of Biomedical Informatics 2024;157:104692 View
  14. Jauk S, Kramer D, Sumerauer S, Veeranki S, Schrempf M, Puchwein P. Machine learning-based delirium prediction in surgical in-patients: a prospective validation study. JAMIA Open 2024;7(3) View
  15. Cabanillas Silva P, Sun H, Rezk M, Roccaro-Waldmeyer D, Fliegenschmidt J, Hulde N, von Dossow V, Meesseman L, Depraetere K, Stieg J, Szymanowsky R, Dahlweid F. Longitudinal Model Shifts of Machine Learning–Based Clinical Risk Prediction Models: Evaluation Study of Multiple Use Cases Across Different Hospitals. Journal of Medical Internet Research 2024;26:e51409 View
  16. Kwon C, Lee B, Kim S, Jeong S, Kim J. Assessing Hwa-byung Vulnerability Using the Hwa-byung Personality Scale: a comparative study of machine learning approaches. Journal of Pharmacopuncture 2024;27(4):358 View
  17. Cho N, Jeong I, Ahn S, Gil H, Kim Y, Park J, Kang S, Lee H. Machine Learning to Assist in Managing Acute Kidney Injury in General Wards: Multicenter Retrospective Study. Journal of Medical Internet Research 2025;27:e66568 View
  18. Kwun J, Ahn H, Kang S, Yoo S, Kim S, Song W, Hyun J, Oh J, Baek G, Suh J. Developing a Machine Learning Model for Predicting 30-Day Major Adverse Cardiac and Cerebrovascular Events in Patients Undergoing Noncardiac Surgery: Retrospective Study. Journal of Medical Internet Research 2025;27:e66366 View

Conference Proceedings

  1. Antweiler D, Gallusser F, Fuchs G. 2023 Workshop on Visual Analytics in Healthcare (VAHC). Multi-Task Transformer Visualization to build Trust for Clinical Outcome Prediction View
  2. Nolte D, Ghosh S, Pal R. 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Efficient Normalized Conformal Prediction and Uncertainty Quantification for Anti-Cancer Drug Sensitivity Prediction with Deep Regression Forests View