AIMC Topic: Clinical Deterioration
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Real-Time Machine Learning Alerts to Prevent Escalation of Care: A Nonrandomized Clustered Pragmatic Clinical Trial.
Critical care medicine
Feb 21, 2024
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...
Early identification of patients admitted to hospital for covid-19 at risk of clinical deterioration: model development and multisite external validation study.
BMJ (Clinical research ed.)
Feb 17, 2022
OBJECTIVE: To create and validate a simple and transferable machine learning model from electronic health record data to accurately predict clinical deterioration in patients with covid-19 across institutions, through use of a novel paradigm for mode...
Young Adult
Adult
Male
Machine Learning
Hospitals
Hospitalization
COVID-19
Adolescent
Aged
Prognosis
Middle Aged
Aged, 80 and over
Humans
Retrospective Studies
Risk Assessment
Electronic Health Records
Female
SARS-CoV-2
Linear Models
Clinical Decision Rules
ROC Curve
Clinical Deterioration
Predictive Value of Tests
Area Under Curve
A machine learning model for predicting deterioration of COVID-19 inpatients.
Scientific reports
Feb 16, 2022
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
Feb 4, 2021
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...
Comparing the predictive ability of a commercial artificial intelligence early warning system with physician judgement for clinical deterioration in hospitalised general internal medicine patients: a prospective observational study.
BMJ open
Oct 10, 2019
OBJECTIVE: Our study compares physician judgement with an automated early warning system (EWS) for predicting clinical deterioration of hospitalised general internal medicine patients.
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
Aug 7, 2025
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
Jun 1, 2025
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
Mar 1, 2025
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 ...
Effectiveness of an Artificial Intelligence-Enabled Intervention for Detecting Clinical Deterioration.
JAMA internal medicine
May 1, 2024
IMPORTANCE: Inpatient clinical deterioration is associated with substantial morbidity and mortality but may be easily missed by clinicians. Early warning scores have been developed to alert clinicians to patients at high risk of clinical deterioratio...
Systematic review and longitudinal analysis of implementing Artificial Intelligence to predict clinical deterioration in adult hospitals: what is known and what remains uncertain.
Journal of the American Medical Informatics Association : JAMIA
Jan 18, 2024
OBJECTIVE: To identify factors influencing implementation of machine learning algorithms (MLAs) that predict clinical deterioration in hospitalized adult patients and relate these to a validated implementation framework.
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