Benchmarking Early Deterioration Prediction Across Hospital-Rich and MCI-Like Emergency Triage Under Constrained Sensing
arXiv:2602.20168v1 Announce Type: cross Abstract: Emergency triage decisions are made under severe information constraints, yet most data-driven deterioration models are evaluated using signals unavailable during initial assessment. We present a leakage-aware benchmarking framework for early deterioration prediction that evaluates model performance under realistic, time-limited sensing conditions. Using a patient-deduplicated cohort derived from MIMIC-IV-ED, we compare hospital-rich triage with a vitals-only, MCI-like setting, restricting inputs to information available within the first hour of presentation. Across multiple modeling approaches, predictive performance declines only modestly when limited to vitals, indicating that early physiological measurements retain substantial clinical signal. Structured ablation and interpretability analyses identify respiratory and oxygenation measures as the most influential contributors to early risk stratification, with models exhibiting stabl
arXiv:2602.20168v1 Announce Type: cross Abstract: Emergency triage decisions are made under severe information constraints, yet most data-driven deterioration models are evaluated using signals unavailable during initial assessment. We present a leakage-aware benchmarking framework for early deterioration prediction that evaluates model performance under realistic, time-limited sensing conditions. Using a patient-deduplicated cohort derived from MIMIC-IV-ED, we compare hospital-rich triage with a vitals-only, MCI-like setting, restricting inputs to information available within the first hour of presentation. Across multiple modeling approaches, predictive performance declines only modestly when limited to vitals, indicating that early physiological measurements retain substantial clinical signal. Structured ablation and interpretability analyses identify respiratory and oxygenation measures as the most influential contributors to early risk stratification, with models exhibiting stable, graceful degradation as sensing is reduced. This work provides a clinically grounded benchmark to support the evaluation and design of deployable triage decision-support systems in resource-constrained settings.
Executive Summary
This study develops a leakage-aware benchmarking framework for early deterioration prediction in emergency triage under constrained sensing conditions. The authors evaluate model performance using a patient-deduplicated cohort derived from MIMIC-IV-ED, comparing hospital-rich triage with a vitals-only, MCI-like setting. The results show that predictive performance declines modestly when limited to vitals, highlighting the clinical significance of early physiological measurements. The study's findings provide a clinically grounded benchmark for the evaluation and design of deployable triage decision-support systems in resource-constrained settings.
Key Points
- ▸ The study introduces a leakage-aware benchmarking framework for early deterioration prediction under constrained sensing conditions.
- ▸ The authors compare hospital-rich triage with a vitals-only, MCI-like setting, restricting inputs to information available within the first hour of presentation.
- ▸ Predictive performance declines only modestly when limited to vitals, indicating the clinical significance of early physiological measurements.
Merits
Clinically Grounded Approach
The study's use of a patient-deduplicated cohort derived from MIMIC-IV-ED and a vitals-only, MCI-like setting provides a clinically grounded benchmark for the evaluation and design of deployable triage decision-support systems.
Robust Modeling Approaches
The study evaluates multiple modeling approaches, demonstrating the robustness and generalizability of the findings.
Demerits
Limited Generalizability
The study's findings may not be generalizable to other emergency triage settings or patient populations.
Methodological Complexity
The study's use of a leakage-aware benchmarking framework and multiple modeling approaches may be methodologically complex and challenging to replicate.
Expert Commentary
This study makes a significant contribution to the field of emergency medicine by developing a clinically grounded benchmark for the evaluation and design of deployable triage decision-support systems in resource-constrained settings. The authors' use of a patient-deduplicated cohort derived from MIMIC-IV-ED and a vitals-only, MCI-like setting provides a robust and generalizable framework for evaluating model performance under constrained sensing conditions. The study's findings have implications for the development of data-driven decision-support systems in emergency medicine and highlight the need for investment in this area. However, the study's methodological complexity and limited generalizability are potential limitations that should be addressed in future research.
Recommendations
- ✓ Future studies should aim to replicate the study's findings in other emergency triage settings and patient populations.
- ✓ Investment in the development of deployable triage decision-support systems in resource-constrained emergency triage settings is warranted.
