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

Machine Learning (3353) Tools, Programs and Algorithms (610) Precision Medicine and Genetics (108) Cardiac Risk and Cardiac Risk Calculators (189) Artificial Intelligence (5060) AI applications in genomics and pathology informatics, including generative AI (47)

Published on in Vol 14 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/75565, first published .
Doctor examines patient's leg for physical therapy and rehabilitation.

Development of Venous Thromboembolism Risk Prediction Models Based on Whole Blood Gene Expression Profiling Using 20 Machine Learning Algorithms: Comprehensive Analysis Study

Development of Venous Thromboembolism Risk Prediction Models Based on Whole Blood Gene Expression Profiling Using 20 Machine Learning Algorithms: Comprehensive Analysis Study

Authors of this article:

Yedong Huang1 Author Orcid Image ;   Xiaoyun Chen2 Author Orcid Image ;   Guannan Bai3 Author Orcid Image ;   Yajun Zhao4 Author Orcid Image ;   Dapeng Kuang5 Author Orcid Image ;   Lin Zhang6, 7 Author Orcid Image ;   Wei Lu8 Author Orcid Image
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Journals

  1. MORISHITA E. Omics approaches to hereditary thrombophilia. Japanese Journal of Thrombosis and Hemostasis 2026;37(5):496 View