AIMC Topic: Clinical Deterioration

Clear Filters Showing 11 to 20 of 26 articles

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...

A machine learning model for predicting deterioration of COVID-19 inpatients.

Scientific reports
The COVID-19 pandemic has been spreading worldwide since December 2019, presenting an urgent threat to global health. Due to the limited understanding of disease progression and of the risk factors for the disease, it is a clinical challenge to predi...

Machine Learning-Based Early Warning Systems for Clinical Deterioration: Systematic Scoping Review.

Journal of medical Internet research
BACKGROUND: Timely identification of patients at a high risk of clinical deterioration is key to prioritizing care, allocating resources effectively, and preventing adverse outcomes. Vital signs-based, aggregate-weighted early warning systems are com...

Understanding Clinicians' Usage Patterns of the CONCERN Early Warning System: Insights from a Multi-Site Pragmatic Cluster Randomized Controlled Trial.

Studies in health technology and informatics
The CONCERN Early Warning System (EWS) uses artificial intelligence (AI) to analyze nursing documentation patterns, predicting hospitalized patients' risk of clinical deterioration. It generates real-time risk scores displayed on the electronic healt...

Development of a deep neural network model for ultra-early neurological deterioration in ischemic stroke and analysis of associated risk factors.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
BACKGROUND: In this study, we established a deep neural network (DNN)-based predictive model, aiming to provide a basis for improving the treatment prognosis of early neurological deterioration (END) in patients with ultra-early ischemic stroke after...

Factors underpinning the performance of implemented artificial intelligence-based patient deterioration prediction systems: reasons for selection and implications for hospitals and researchers.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: The degree to which deployed artificial intelligence-based deterioration prediction algorithms (AI-DPA) differ in their development, the reasons for these differences, and how this may impact their performance remains unclear. Our primary ...