AIMC Topic: Clinical Deterioration

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Comparison of Multimodal Deep Learning Approaches for Predicting Clinical Deterioration in Ward Patients: Observational Cohort Study.

Journal of medical Internet research
BACKGROUND: Implementing machine learning models to identify clinical deterioration in the wards is associated with decreased morbidity and mortality. However, these models have high false positive rates and only use structured data.

AI-Powered early warning systems for clinical deterioration significantly improve patient outcomes: a meta-analysis.

BMC medical informatics and decision making
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...

Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial.

Nature medicine
The COmmunicating Narrative Concerns Entered by RNs (CONCERN) early warning system (EWS) uses real-time nursing surveillance documentation patterns in its machine learning algorithm to identify deterioration risk. We conducted a 1-year, multisite, pr...

Multicenter Development and Prospective Validation of eCARTv5: A Gradient-Boosted Machine-Learning Early Warning Score.

Critical care explorations
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...

Multi-horizon event detection for in-hospital clinical deterioration using dual-channel graph attention network.

International journal of medical informatics
OBJECTIVE: In hospitals globally, the occurrence of clinical deterioration within the hospital setting poses a significant healthcare burden. Rapid clinical intervention becomes a crucial task in such cases. In this research, we propose an end-to-end...

Performance of machine learning versus the national early warning score for predicting patient deterioration risk: a single-site study of emergency admissions.

BMJ health & care informatics
OBJECTIVES: Increasing operational pressures on emergency departments (ED) make it imperative to quickly and accurately identify patients requiring urgent clinical intervention. The widespread adoption of electronic health records (EHR) makes rich fe...

A novel deep learning algorithm for real-time prediction of clinical deterioration in the emergency department for a multimodal clinical decision support system.

Scientific reports
The array of complex and evolving patient data has limited clinical decision making in the emergency department (ED). This study introduces an advanced deep learning algorithm designed to enhance real-time prediction accuracy for integration into a n...

Clinical evaluation of a machine learning-based early warning system for patient deterioration.

CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne
BACKGROUND: The implementation and clinical impact of machine learning-based early warning systems for patient deterioration in hospitals have not been well described. We sought to describe the implementation and evaluation of a multifaceted, real-ti...

Development and Validation of a Deep Learning Model for Prediction of Adult Physiological Deterioration.

Critical care explorations
BACKGROUND: Prediction-based strategies for physiologic deterioration offer the potential for earlier clinical interventions that improve patient outcomes. Current strategies are limited because they operate on inconsistent definitions of deteriorati...

Real-Time Machine Learning Alerts to Prevent Escalation of Care: A Nonrandomized Clustered Pragmatic Clinical Trial.

Critical care medicine
OBJECTIVES: Machine learning algorithms can outperform older methods in predicting clinical deterioration, but rigorous prospective data on their real-world efficacy are limited. We hypothesized that real-time machine learning generated alerts sent d...