Clinically Interpretable Mortality Prediction for ICU Patients with Diabetes and Atrial Fibrillation: A Machine Learning Approach
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913901029883904 |
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| author | Sun, Li Chen, Shuheng Si, Yong Fan, Junyi Pishgar, Maryam Pishgar, Elham Alaei, Kamiar Placencia, Greg |
| author_facet | Sun, Li Chen, Shuheng Si, Yong Fan, Junyi Pishgar, Maryam Pishgar, Elham Alaei, Kamiar Placencia, Greg |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_15901 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Clinically Interpretable Mortality Prediction for ICU Patients with Diabetes and Atrial Fibrillation: A Machine Learning Approach Sun, Li Chen, Shuheng Si, Yong Fan, Junyi Pishgar, Maryam Pishgar, Elham Alaei, Kamiar Placencia, Greg Machine Learning 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. |
| title | Clinically Interpretable Mortality Prediction for ICU Patients with Diabetes and Atrial Fibrillation: A Machine Learning Approach |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.15901 |