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

Autor:	Tsai FF; Department of Anesthesiology, National Taiwan University Hospital, Taipei, Taiwan., Chang YC; Graduate Institute of Data Science, Taipei Medical University, Taipei, Taiwan.; Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei, Taiwan.; Clinical Data Center, Office of Data Science, Taipei Medical University, Taipei, Taiwan., Chiu YW; Graduate Institute of Data Science, Taipei Medical University, Taipei, Taiwan., Sheu BC; Medical Research Department, College of Medicine, National Taiwan University, Taipei, Taiwan.; Department of Obstetrics and Gynecology, College of Medicine, National Taiwan University, Taipei, Taiwan., Hsu MH; Graduate Institute of Data Science, Taipei Medical University, Taipei, Taiwan.; Clinical Data Center, Office of Data Science, Taipei Medical University, Taipei, Taiwan., Yeh HM; Department of Anesthesiology, National Taiwan University Hospital, Taipei, Taiwan.
Jazyk:	angličtina
Zdroj:	JMIR formative research [JMIR Form Res] 2024 Aug 21; Vol. 8, pp. e54097. Date of Electronic Publication: 2024 Aug 21.
DOI:	10.2196/54097
Abstrakt:	Background: Preoperative evaluation is important, and this study explored the application of machine learning methods for anesthetic risk classification and the evaluation of the contributions of various factors. To minimize the effects of confounding variables during model training, we used a homogenous group with similar physiological states and ages undergoing similar pelvic organ-related procedures not involving malignancies. Objective: Data on women of reproductive age (age 20-50 years) who underwent gestational or gynecological surgery between January 1, 2017, and December 31, 2021, were obtained from the National Taiwan University Hospital Integrated Medical Database. Methods: We first performed an exploratory analysis and selected key features. We then performed data preprocessing to acquire relevant features related to preoperative examination. To further enhance predictive performance, we used the log-likelihood ratio algorithm to generate comorbidity patterns. Finally, we input the processed features into the light gradient boosting machine (LightGBM) model for training and subsequent prediction. Results: A total of 10,892 patients were included. Within this data set, 9893 patients were classified as having low anesthetic risk (American Society of Anesthesiologists physical status score of 1-2), and 999 patients were classified as having high anesthetic risk (American Society of Anesthesiologists physical status score of >2). The area under the receiver operating characteristic curve of the proposed model was 0.6831. Conclusions: By combining comorbidity information and clinical laboratory data, our methodology based on the LightGBM model provides more accurate predictions for anesthetic risk classification. Trial Registration: Research Ethics Committee of the National Taiwan University Hospital 202204010RINB; https://www.ntuh.gov.tw/RECO/Index.action. (©Feng-Fang Tsai, Yung-Chun Chang, Yu-Wen Chiu, Bor-Ching Sheu, Min-Huei Hsu, Huei-Ming Yeh. Originally published in JMIR Formative Research (https://formative.jmir.org), 21.08.2024.)
Databáze:	MEDLINE
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