@Article{info:doi/10.2196/55046,
author="Ding, Zhendong
and Zhang, Linan
and Zhang, Yihan
and Yang, Jing
and Luo, Yuheng
and Ge, Mian
and Yao, Weifeng
and Hei, Ziqing
and Chen, Chaojin",
title="A Supervised Explainable Machine Learning Model for Perioperative Neurocognitive Disorder in Liver-Transplantation Patients and External Validation on the Medical Information Mart for Intensive Care IV Database: Retrospective Study",
journal="J Med Internet Res",
year="2025",
month="Jan",
day="15",
volume="27",
pages="e55046",
keywords="machine learning; risk factors; liver transplantation; perioperative neurocognitive disorders; MIMIC-Ⅳ database; external validation",
abstract="Background: Patients undergoing liver transplantation (LT) are at risk of perioperative neurocognitive dysfunction (PND), which significantly affects the patients' prognosis. Objective: This study used machine learning (ML) algorithms with an aim to extract critical predictors and develop an ML model to predict PND among LT recipients. Methods: In this retrospective study, data from 958 patients who underwent LT between January 2015 and January 2020 were extracted from the Third Affiliated Hospital of Sun Yat-sen University. Six ML algorithms were used to predict post-LT PND, and model performance was evaluated using area under the receiver operating curve (AUC), accuracy, sensitivity, specificity, and F1-scores. The best-performing model was additionally validated using a temporal external dataset including 309 LT cases from February 2020 to August 2022, and an independent external dataset extracted from the Medical Information Mart for Intensive Care Ⅳ (MIMIC-Ⅳ) database including 325 patients. Results: In the development cohort, 201 out of 751 (33.5{\%}) patients were diagnosed with PND. The logistic regression model achieved the highest AUC (0.799) in the internal validation set, with comparable AUC in the temporal external (0.826) and MIMIC-Ⅳ validation sets (0.72). The top 3 features contributing to post-LT PND diagnosis were the preoperative overt hepatic encephalopathy, platelet level, and postoperative sequential organ failure assessment score, as revealed by the Shapley additive explanations method. Conclusions: A real-time logistic regression model-based online predictor of post-LT PND was developed, providing a highly interoperable tool for use across medical institutions to support early risk stratification and decision making for the LT recipients. ",
issn="1438-8871",
doi="10.2196/55046",
url="https://www.jmir.org/2025/1/e55046",
url="https://doi.org/10.2196/55046",
url="http://www.ncbi.nlm.nih.gov/pubmed/39813086"
}