Authors - Menna Elgabry, Ali Hamdi Abstract - Mortality prediction for intensive care unit (ICU) patients with alcohol-related disorders remains insufficiently explored despite the distinct clinical characteristics and elevated risk profile of this population. Unlike general ICU cohorts, these patients often present with impaired physiological function, frequent complications, and poorer overall outcomes. However, few research works have taken this patient group into account for mortality prediction. This study addresses the gap by developing mortality prediction models specifically for ICU patients with alcohol-related disorders using multimodal electronic health record data. To capture the complex clinical status of patients, we integrate six major data modalities in the first 24 hours after admission, including demographics, diagnoses, medications, procedures, laboratory results/vital signs, and patient outputs. A refined preprocessing pipeline was used to harmonize and process heterogeneous input data. In addition, severe class imbalance is another challenging issue in resolving this mortality predict task. Therefore, our work examines systematically several rebalancing strategies: no resampling, oversampling, undersampling, and SMOTENC. Evaluated on both MIMIC-III and MIMIC-IV databases, our proposed rebalanced multimodal data approach is effective for tackling the task. Indeed, the experimental results show that CatBoost with random undersampling provides the most consistent and balanced effectiveness. Furthermore, multimodal analysis demonstrates that combining diagnoses, laboratory results/vital signs, and medications substantially improves prediction, while integrating all modalities achieves the best overall performance.
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