Clinically Interpretable Mortality Prediction for ICU Patients with Diabetes and Atrial Fibrillation: A Machine Learning Approach

• URL: http://arxiv.org/abs/2506.15901v1
• Date: Wed, 18 Jun 2025 22:04:12 GMT
• Title: Clinically Interpretable Mortality Prediction for ICU Patients with Diabetes and Atrial Fibrillation: A Machine Learning Approach
• Authors: Li Sun, Shuheng Chen, Yong Si, Junyi Fan, Maryam Pishgar, Elham Pishgar, Kamiar Alaei, Greg Placencia,
• Abstract summary: Patients with diabetes mellitus (DM) and atrial fibrillation (AF) face elevated mortality in intensive care units (ICUs)<n>This study developed an interpretable machine learning model predicting 28-day mortality in ICU patients with concurrent DM and AF.
• Score: 3.5626691568652507
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Background: Patients with both diabetes mellitus (DM) and atrial fibrillation (AF) face elevated mortality in intensive care units (ICUs), yet models targeting this high-risk group remain limited. Objective: To develop an interpretable machine learning (ML) model predicting 28-day mortality in ICU patients with concurrent DM and AF using early-phase clinical data. Methods: A retrospective cohort of 1,535 adult ICU patients with DM and AF was extracted from the MIMIC-IV database. Data preprocessing involved median/mode imputation, z-score normalization, and early temporal feature engineering. A two-step feature selection pipeline-univariate filtering (ANOVA F-test) and Random Forest-based multivariate ranking-yielded 19 interpretable features. Seven ML models were trained with stratified 5-fold cross-validation and SMOTE oversampling. Interpretability was assessed via ablation and Accumulated Local Effects (ALE) analysis. Results: Logistic regression achieved the best performance (AUROC: 0.825; 95% CI: 0.779-0.867), surpassing more complex models. Key predictors included RAS, age, bilirubin, and extubation. ALE plots showed intuitive, non-linear effects such as age-related risk acceleration and bilirubin thresholds. Conclusion: This interpretable ML model offers accurate risk prediction and clinical insights for early ICU triage in patients with DM and AF.

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