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A Machine Learning-based Prediction Model for Postoperative Delirium in Cardiac Valve Surgery Using Electronic Health Records by Qiuying Li; Jiaxin Li; Jiansong Chen; Xu Zhao; Jian Zhuang; Guoping Zhong; Yamin Song; Liming Lei is a Medicine article available to read on EtoBox.

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## Background Previous models for predicting delirium after cardiac surgery remained inadequate. This study aimed to develop and validate a machine learning-based prediction model for postoperative delirium (POD) in cardiac valve surgery patients. ## Methods The electronic medical information of the cardiac surgical intensive care unit (CSICU) was extracted from a tertiary and major referral hospital in southern China over 1 year, from June 2019 to June 2020. A total of 507 patients admitted to the CSICU after cardiac valve surgery were included in this study. Seven classical machine learning algorithms (Random Forest Classifier, Logistic Regression, Support Vector Machine Classifier, K-nearest Neighbors Classifier, Gaussian Naive Bayes, Gradient Boosting Decision Tree, and Perceptron.) were used to develop delirium prediction models under full (q = 31) and selected (q = 19) feature sets, respectively. ## Result The Random Forest classifier performs exceptionally well in both feature datasets, with an Area Under the Curve (AUC) of 0.92 for the full feature dataset and an AUC of 0.86 for the selected feature dataset. Additionally, it achieves a relatively lower Expected Calibration

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Author

Qiuying Li; Jiaxin Li; Jiansong Chen; Xu Zhao; Jian Zhuang; Guoping Zhong; Yamin Song; Liming Lei

Publisher

BioMed Central

Published

2024

Language

EN

Field

Medicine (Health Sciences)