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

Formative Evaluation of Digital Health Interventions (2491) Clinical Information and Decision Making (1434) Machine Learning (1501) Patient Monitoring and Anesthesia Information Management Systems (13) Artificial Intelligence in Surgery and Perioperative Medicine (27) Obstetrics, Gynecology, and Reproductive Medicine (235)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/54097, first published .
Machine Learning Model for Anesthetic Risk Stratification for Gynecologic and Obstetric Patients: Cross-Sectional Study Outlining a Novel Approach for Early Detection

Machine Learning Model for Anesthetic Risk Stratification for Gynecologic and Obstetric Patients: Cross-Sectional Study Outlining a Novel Approach for Early Detection

Machine Learning Model for Anesthetic Risk Stratification for Gynecologic and Obstetric Patients: Cross-Sectional Study Outlining a Novel Approach for Early Detection

Authors of this article:

Feng-Fang Tsai1 Author Orcid Image ;   Yung-Chun Chang2, 3, 4 Author Orcid Image ;   Yu-Wen Chiu2 Author Orcid Image ;   Bor-Ching Sheu5, 6 Author Orcid Image ;   Min-Huei Hsu2, 4 Author Orcid Image ;   Huei-Ming Yeh1 Author Orcid Image
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Journals

  1. Ocampo Osorio F, Alzate-Ricaurte S, Mejia Vallecilla T, Cruz-Suarez G. The anesthesiologist’s guide to critically assessing machine learning research: a narrative review. BMC Anesthesiology 2024;24(1) View