ArticleNPJ digital medicine2023
Predicting patient decompensation from continuous physiologic monitoring in the emergency department.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- [Research progress in artificial intelligence for the diagnosis and management of diseases in preterm infants].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2026Review
- From scores to signals: evolution and innovations in pediatric early warning systems.Acute and critical care · 2026Article
- Real-time prediction of cardiorespiratory deterioration during paediatric critical care transport using interpretable machine learning.PLOS digital health · 2026Article
- Large language models in emergency and critical care medicine: a comprehensive review of applications, challenges, and future directions.Burns & trauma · 2026Review
- Prognostic Biomarkers for Predicting Decompensation in Alcoholic and Nonalcoholic Patients With Compensated Cirrhosis: An Umbrella Review.Biochemistry research international · 2026Review
- Nurses as guardians of time: the hidden clinical value of continuous care in geriatrics.Frontiers in public health · 2026Review
- Visual scoring of photoplethysmography morphology for risk stratification in hemorrhagic shock: a prospective observational study.Annals of intensive care · 2026Article
- Cardiac health assessment across scenarios and devices using a multimodal foundation model pretrained on data from 1.7 million individuals.Nature machine intelligence · 2026Article
- Clinical validation of an AI-based blood testing device for diagnosis and prognosis of acute infection and sepsis.Nature medicine · 2025Observational
- PhysioJEPA: Joint Embedding Representations of Physiological Signals for Real Time Risk Estimation in the Intensive Care Unit.Proceedings of machine learning research · 2025Article
- Development and validation of a clinical wearable deep learning based continuous inhospital deterioration prediction model.Nature communications · 2025Article
- Preoperative risk prediction of major cardiovascular events in noncardiac surgery using the 12-lead electrocardiogram: an explainable deep learning approach.British journal of anaesthesia · 2025Observational
- Identification of physiological adverse events using continuous vital signs monitoring during paediatric critical care transport: A novel data-driven approach.PLOS digital health · 2025Article
- Transforming sepsis management: AI-driven innovations in early detection and tailored therapies.Critical care (London, England) · 2025Review
- The Epidemiology of Emergency Calls in a Tertiary Emergency Department for Admitted Patients: A TECOR Study.Healthcare (Basel, Switzerland) · 2025Article
- In limbo: Physiological monitoring of emergency department boarders.The American journal of emergency medicine · 2025Article
- A framework for integrating artificial intelligence for clinical care with continuous therapeutic monitoring.Nature biomedical engineering · 2025Review
- Dynamic mortality prediction in critically Ill children during interhospital transports to PICUs using explainable AI.NPJ digital medicine · 2025Article
- Overview of Wearable Healthcare Devices for Clinical Decision Support in the Prehospital Setting.Sensors (Basel, Switzerland) · 2024Review
- Machine learning for early dynamic prediction of functional outcome after stroke.Communications medicine · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Anticipation of clinical decompensation is essential for effective emergency and critical care. In this study, we develop a multimodal machine learning approach to predict the onset of new vital sign abnormalities (tachycardia, hypotension, hypoxia) in ED patients with normal initial vital signs. Our method combines standard triage data (vital signs, demographics, chief complaint) with features derived from a brief period of continuous physiologic monitoring, extracted via both conventional signal processing and transformer-based deep learning on ECG and PPG waveforms. We study 19,847 adult ED visits, divided into training (75%), validation (12.5%), and a chronologically sequential held-out test set (12.5%). The best-performing models use a combination of engineered and transformer-derived features, predicting in a 90-minute window new tachycardia with AUROC of 0.836 (95% CI, 0.800-0.870), new hypotension with AUROC 0.802 (95% CI, 0.747-0.856), and new hypoxia with AUROC 0.713 (95% CI, 0.680-0.745), in all cases significantly outperforming models using only standard triage data. Salient features include vital sign trends, PPG perfusion index, and ECG waveforms. This approach could improve the triage of apparently stable patients and be applied continuously for the prediction of near-term clinical deterioration.
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Registered trials
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