TY  - JOUR
AU  - Tsai, Feng-Fang
AU  - Chang, Yung-Chun
AU  - Chiu, Yu-Wen
AU  - Sheu, Bor-Ching
AU  - Hsu, Min-Huei
AU  - Yeh, Huei-Ming
PY  - 2024
DA  - 2024/8/21
TI  - Machine Learning Model for Anesthetic Risk Stratification for Gynecologic and Obstetric Patients: Cross-Sectional Study Outlining a Novel Approach for Early Detection
JO  - JMIR Form Res
SP  - e54097
VL  - 8
KW  - gradient boosting machine
KW  - comorbidity
KW  - gynecological and obstetric procedure
KW  - ASA classification
KW  - American Society of Anesthesiologists
KW  - preoperative evaluation
KW  - machine learning
KW  - machine learning model
KW  - gynecology
KW  - obstetrics
KW  - early detection
KW  - artificial intelligence
KW  - physiological
KW  - gestational
KW  - anesthetic risk
KW  - clinical laboratory data
KW  - laboratory data
KW  - risk
KW  - risk classification
AB  - 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 
SN  - 2561-326X
UR  - https://formative.jmir.org/2024/1/e54097
UR  - https://doi.org/10.2196/54097
UR  - http://www.ncbi.nlm.nih.gov/pubmed/38991090
DO  - 10.2196/54097
ID  - info:doi/10.2196/54097
ER  -