AIMC Topic: Clinical Deterioration

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Effectiveness of an Artificial Intelligence-Enabled Intervention for Detecting Clinical Deterioration.

JAMA internal medicine
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
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.

A Simulated Prospective Evaluation of a Deep Learning Model for Real-Time Prediction of Clinical Deterioration Among Ward Patients.

Critical care medicine
OBJECTIVES: The National Early Warning Score, Modified Early Warning Score, and quick Sepsis-related Organ Failure Assessment can predict clinical deterioration. These scores exhibit only moderate performance and are often evaluated using aggregated ...

Detecting Patient Deterioration Using Artificial Intelligence in a Rapid Response System.

Critical care medicine
OBJECTIVES: As the performance of a conventional track and trigger system in a rapid response system has been unsatisfactory, we developed and implemented an artificial intelligence for predicting in-hospital cardiac arrest, denoted the deep learning...

Advancing In-Hospital Clinical Deterioration Prediction Models.

American journal of critical care : an official publication, American Association of Critical-Care Nurses
BACKGROUND: Early warning systems lack robust evidence that they improve patients' outcomes, possibly because of their limitation of predicting binary rather than time-to-event outcomes.