ArticleNPJ digital medicine2026
Artificial Intelligence-powered tiered early warning framework addressing high false alarm rates for in-hospital mortality prediction.
Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Multicenter evaluation of four large language models for automated spine imaging diagnosis.NPJ digital medicine · 2026Article
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Authors and funding
32 authors.
Funding
Abstract
Alert fatigue remains a major barrier to the effective deployment of predictive models in emergency care, particularly in the context of rare but critical outcomes such as in-hospital mortality (IHM), which often occurs in less than 5.0% of patients admitted from the emergency department (ED). Severe class imbalance leads to low positive predictive value (PPV), undermining the clinical utility of even high-performance predictive models. To address this issue, we propose AI-TEW (Artificial Intelligence-powered Tiered Early Warning), a novel two-stage early warning framework designed to reduce false alarms and improve clinical interpretability. In Stage 1, a robust machine learning model was developed and validated using data from 174,292 ED visits across three hospitals in China and the United States. The model demonstrated strong discriminative ability for IHM prediction, achieving AUROCs ranging from 0.84 (95% CI, 0.81-0.86) to 0.91 (95% CI, 0.90-0.91) in internal and external validation cohorts. In Stage 2, AI-TEW implements a tiered risk stratification strategy by optimizing decision thresholds to prioritize high-risk patients, thereby increasing PPV from baseline levels of 9.8-18.8% to 32.5-40.5% across sites, while maintaining a high negative predictive value (NPV) of over 98% for low-risk individuals. To further refine alert precision, a knowledge-based filtering layer is introduced, leveraging large language models (LLM) to interpret patient-specific risk factors derived from SHAP (Shapley Additive exPlanations) method. Integrating explainable AI with clinical reasoning enhances contextual understanding and reduces spurious alerts, leading to an 11.53% increase in PPV in external validation (p = 0.0092 for MedGemma). By integrating improved predictive efficiency with interpretable, knowledge-informed filtering, AI-TEW reduces alert burden while supporting timely clinical intervention, demonstrating a promising approach to mitigating the impact of class imbalance in emergency risk prediction.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.