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[2602.20168] Benchmarking Early Deterioration Prediction Across Hospital-Rich and MCI-Like Emergency Triage Under Constrained Sensing
Summary
This article presents a benchmarking framework for early deterioration prediction in emergency triage, comparing hospital-rich settings with constrained sensing environments.
Why It Matters
Understanding how to effectively predict patient deterioration under limited information is crucial for improving emergency care outcomes. This research provides insights into model performance and highlights the importance of early physiological measurements, which can inform the design of decision-support systems in resource-constrained settings.
Key Takeaways
- The study introduces a leakage-aware benchmarking framework for evaluating deterioration prediction models.
- Predictive performance remains relatively stable even with limited input data, emphasizing the value of early physiological measurements.
- Respiratory and oxygenation metrics are identified as key factors in early risk stratification.
- The findings support the development of triage decision-support systems in emergency settings with constrained resources.
- Models demonstrate graceful degradation in performance as sensing capabilities are reduced.
Computer Science > Computers and Society arXiv:2602.20168 (cs) [Submitted on 9 Feb 2026] Title:Benchmarking Early Deterioration Prediction Across Hospital-Rich and MCI-Like Emergency Triage Under Constrained Sensing Authors:KMA Solaiman, Joshua Sebastian, Karma Tobden View a PDF of the paper titled Benchmarking Early Deterioration Prediction Across Hospital-Rich and MCI-Like Emergency Triage Under Constrained Sensing, by KMA Solaiman and 2 other authors View PDF HTML (experimental) 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 ...