AIMC Topic: Early Warning Score
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Predicting patient deterioration with physiological data using AI: systematic review protocol.
BMJ health & care informatics
Aug 5, 2025
INTRODUCTION: The second iteration of the National Early Warning Score has been adopted widely within the UK and internationally. It uses routinely collected physiological measurements to standardise the assessment and response to acute illness. Its ...
Early warning and stratification of the elderly cardiopulmonary dysfunction-related diseases: multicentre prospective study protocol.
BMJ open
Jul 5, 2025
INTRODUCTION: In China, there is a lack of standardised clinical imaging databases for multidimensional evaluation of cardiopulmonary diseases. To address this gap, this study protocol launched a project to build a clinical imaging technology integra...
Development and validation of a transformer model-based early warning score for real-time prediction of adverse outcomes in the emergency department.
Scientific reports
Jul 2, 2025
This study aimed to develop and validate a transformer-based early warning score (TEWS) system for predicting adverse events (AEs) in the emergency department (ED). We conducted a retrospective study analyzing adult ED visits at a tertiary hospital. ...
Machine learning to improve predictive performance of prehospital early warning scores.
Scientific reports
Jul 1, 2025
Early warning scores are used to assess acute patients' risk of being in a critical situation, allowing for early appropriate treatment, avoiding critical outcomes. The early warning scores use changes in vital signs to provide an assessment, however...
AI-Powered early warning systems for clinical deterioration significantly improve patient outcomes: a meta-analysis.
BMC medical informatics and decision making
Jun 2, 2025
BACKGROUND: Clinical deterioration is often preceded by subtle physiological changes that, if unheeded, can lead to adverse patient outcomes. The precision of traditional scoring systems in detecting these precursors has limitations, prompting the ex...
Influence of the CONCERN Early Warning System on Unanticipated ICU Transfers, In-Hospital Mortality, and Length of Stay: Results from a Multi-site Pragmatic Randomized Controlled Clinical Trial.
AMIA ... Annual Symposium proceedings. AMIA Symposium
May 22, 2025
Communicating Narrative Concerns Entered by RNs Early Warning System (CONCERN EWS) is a machine-learning predictive model that leverages nursing surveillance documentation patterns to predict deterioration risks for hospitalized patients. In a retros...
A comparative study of neural network architectures for vital signs monitoring based on the national early warning systems (NEWS).
Health informatics journal
Apr 25, 2025
The study aims to assess the efficacy of various neural network architectures in predicting the National Early Warning Systems (NEWS) score, using vital signs, to enhance early warning and monitoring in clinical settings. A comparative evaluation o...
Constructing an early warning model for elderly sepsis patients based on machine learning.
Scientific reports
Mar 27, 2025
Sepsis is a serious threat to human life. Early prediction of high-risk populations for sepsis is necessary especially in elderly patients. Artificial intelligence shows benefits in early warning. The aim of the study was to construct an early machin...
Multicenter Development and Prospective Validation of eCARTv5: A Gradient-Boosted Machine-Learning Early Warning Score.
Critical care explorations
Mar 26, 2025
BACKGROUND: Early detection of clinical deterioration using machine-learning early warning scores may improve outcomes. However, most implemented scores were developed using logistic regression, only underwent retrospective validation, and were not t...
Machine Learning-Based Pediatric Early Warning Score: Patient Outcomes in a Pre- Versus Post-Implementation Study, 2019-2023.
Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies
Feb 6, 2025
OBJECTIVES: To describe the deployment of pediatric Calculated Assessment of Risk and Triage (pCART), a machine learning (ML) model to predict the risk of the direct ward to the ICU transfer within 12 hours, and the associated improved outcomes among...
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