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

Machine Learning (1501) Electronic Health Records (1068) Clinical Information and Decision Making (1434) Innovations and Technology in Cancer Care (431) Decision Support for Health Professionals (1210)

Published on in Vol 9, No 2 (2021): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23147, first published .
Prediction of Prolonged Length of Hospital Stay After Cancer Surgery Using Machine Learning on Electronic Health Records: Retrospective Cross-sectional Study

Prediction of Prolonged Length of Hospital Stay After Cancer Surgery Using Machine Learning on Electronic Health Records: Retrospective Cross-sectional Study

Prediction of Prolonged Length of Hospital Stay After Cancer Surgery Using Machine Learning on Electronic Health Records: Retrospective Cross-sectional Study

Authors of this article:

Yong-Yeon Jo1 Author Orcid Image ;   JaiHong Han2 Author Orcid Image ;   Hyun Woo Park1 Author Orcid Image ;   Hyojung Jung1 Author Orcid Image ;   Jae Dong Lee1 Author Orcid Image ;   Jipmin Jung3 Author Orcid Image ;   Hyo Soung Cha3 Author Orcid Image ;   Dae Kyung Sohn4 Author Orcid Image ;   Yul Hwangbo1 Author Orcid Image
Article Authors Cited by (15) Tweetations (14) Metrics

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  10. Zhang G, Liu X, Hu Y, Luo Q, Ruan L, Xie H, Zeng Y. Development and comparison of machine-learning models for predicting prolonged postoperative length of stay in lung cancer patients following video-assisted thoracoscopic surgery. Asia-Pacific Journal of Oncology Nursing 2024;11(6):100493 View
  11. Brydges G, Uppal A, Gottumukkala V. Application of Machine Learning in Predicting Perioperative Outcomes in Patients with Cancer: A Narrative Review for Clinicians. Current Oncology 2024;31(5):2727 View
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