@article{99cb71877a0a4dd5af259f641a6196c3,
title = "Multi-center validation of machine learning model for preoperative prediction of postoperative mortality",
abstract = "Accurate prediction of postoperative mortality is important for not only successful postoperative patient care but also for information-based shared decision-making with patients and efficient allocation of medical resources. This study aimed to create a machine-learning prediction model for 30-day mortality after a non-cardiac surgery that adapts to the manageable amount of clinical information as input features and is validated against multi-centered rather than single-centered data. Data were collected from 454,404 patients over 18 years of age who underwent non-cardiac surgeries from four independent institutions. We performed a retrospective analysis of the retrieved data. Only 12–18 clinical variables were used for model training. Logistic regression, random forest classifier, extreme gradient boosting (XGBoost), and deep neural network methods were applied to compare the prediction performances. To reduce overfitting and create a robust model, bootstrapping and grid search with tenfold cross-validation were performed. The XGBoost method in Seoul National University Hospital (SNUH) data delivers the best performance in terms of the area under receiver operating characteristic curve (AUROC) (0.9376) and the area under the precision-recall curve (0.1593). The predictive performance was the best when the SNUH model was validated with Ewha Womans University Medical Center data (AUROC, 0.941). Preoperative albumin, prothrombin time, and age were the most important features in the model for each hospital. It is possible to create a robust artificial intelligence prediction model applicable to multiple institutions through a light predictive model using only minimal preoperative information that can be automatically extracted from each hospital.",
author = "Lee, {Seung Wook} and Lee, {Hyung Chul} and Jungyo Suh and Lee, {Kyung Hyun} and Heonyi Lee and Suryang Seo and Kim, {Tae Kyong} and Lee, {Sang Wook} and Kim, {Yi Jun}",
note = "Funding Information: The authors would like to thank everyone who participated in this study. This study was conducted as a team project during the training course of medical artificial intelligence experts organized by the Korea Human Resource Development Institute for Health & Welfare. The authors would like to thank all the staff members of the Korea Human Resource Development Institute for Health & Welfare, which organized the team project to make this study possible. This research was supported by a grant for the Medical data-driven hospital support project through the Korea Health Information Service (KHIS), funded by the Ministry of Health & Welfare, Republic of Korea, and the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)(No. NRF-2020R1C1C1011369). Funding Information: The authors would like to thank everyone who participated in this study. This study was conducted as a team project during the training course of medical artificial intelligence experts organized by the Korea Human Resource Development Institute for Health & Welfare. The authors would like to thank all the staff members of the Korea Human Resource Development Institute for Health & Welfare, which organized the team project to make this study possible. This research was supported by a grant for the Medical data-driven hospital support project through the Korea Health Information Service (KHIS), funded by the Ministry of Health & Welfare, Republic of Korea, and the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)(No. NRF-2020R1C1C1011369). Publisher Copyright: {\textcopyright} 2022, The Author(s).",
year = "2022",
month = dec,
doi = "10.1038/s41746-022-00625-6",
language = "English",
volume = "5",
journal = "npj Digital Medicine",
issn = "2398-6352",
publisher = "Nature Publishing Group",
number = "1",
}