Transparent Early ICU Mortality Prediction with Clinical Transformer and Per-Case Modality Attribution
This addresses the need for interpretable and robust predictive models in clinical settings to support timely interventions and resource allocation, though it is incremental in combining existing methods for transparency.
The paper tackled the problem of early ICU mortality prediction by developing a transparent multimodal ensemble that fuses physiological time-series and clinical notes, achieving improved discrimination (AUPRC 0.565 vs. 0.526; AUROC 0.891 vs. 0.876) on the MIMIC-III benchmark.
Early identification of intensive care patients at risk of in-hospital mortality enables timely intervention and efficient resource allocation. Despite high predictive performance, existing machine learning approaches lack transparency and robustness, limiting clinical adoption. We present a lightweight, transparent multimodal ensemble that fuses physiological time-series measurements with unstructured clinical notes from the first 48 hours of an ICU stay. A logistic regression model combines predictions from two modality-specific models: a bidirectional LSTM for vitals and a finetuned ClinicalModernBERT transformer for notes. This traceable architecture allows for multilevel interpretability: feature attributions within each modality and direct per-case modality attributions quantifying how vitals and notes influence each decision. On the MIMIC-III benchmark, our late-fusion ensemble improves discrimination over the best single model (AUPRC 0.565 vs. 0.526; AUROC 0.891 vs. 0.876) while maintaining well-calibrated predictions. The system remains robust through a calibrated fallback when a modality is missing. These results demonstrate competitive performance with reliable, auditable risk estimates and transparent, predictable operation, which together are crucial for clinical use.