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Development and Validation of a Machine Learning Model for Early Prediction of Delirium in Intensive Care Units Using Continuous Physiological Data: Retrospective Study

Development and Validation of a Machine Learning Model for Early Prediction of Delirium in Intensive Care Units Using Continuous Physiological Data: Retrospective Study

Continuous variables were compared using 2-tailed independent t tests for normally distributed data or Mann-Whitney U tests for nonnormally distributed data. Normality was assessed using the Shapiro-Wilk test. In addition, we analyzed correlations so that we could intuitively examine the relationship between each variable we considered and the outcome. The AUROC, sensitivity, positive predictive value, and accuracy at a threshold were measured to compare the performance of different models.

Chanmin Park, Changho Han, Su Kyeong Jang, Hyungjun Kim, Sora Kim, Byung Hee Kang, Kyoungwon Jung, Dukyong Yoon

J Med Internet Res 2025;27:e59520

Machine Learning–Based Explainable Automated Nonlinear Computation Scoring System for Health Score and an Application for Prediction of Perioperative Stroke: Retrospective Study

Machine Learning–Based Explainable Automated Nonlinear Computation Scoring System for Health Score and an Application for Prediction of Perioperative Stroke: Retrospective Study

Continuous variables were analyzed using the Student t test and the Mann-Whitney U test to determine the significance of differences between the 2 groups. Categorical variables in both datasets were evaluated using the chi-square test to investigate the presence of significant associations or discrepancies between the categories.

Mi-Young Oh, Hee-Soo Kim, Young Mi Jung, Hyung-Chul Lee, Seung-Bo Lee, Seung Mi Lee

J Med Internet Res 2025;27:e58021