Objectives To develop and evaluate a high-dimensional, data-driven model to identify
patients at high risk of clinical deterioration from routinely collected electronic health record …

Objective Critical events are common and difficult to predict among infants with congenital
heart disease and are associated with mortality and long-term sequelae. We aimed to …

Starting in 2008, the adoption of electronic health records (EHR) in US hospitals has grown
exponentially from 9% to 96% of hospitals, while also exhibiting substantial uptake in …

Objective: Although many clinical metrics are associated with proximity to decompensation
in heart failure (HF), none are individually accurate enough to risk-stratify HF patients on a …

Background Early recognition of deterioration events is crucial to improve clinical outcomes.
For this purpose, we developed a deep-learning-based pediatric early-warning system …

OBJECTIVES: Develop and deploy a disease cohort-based machine learning algorithm for
timely identification of hospitalized pediatric patients at risk for clinical deterioration that …

Clinical decision-making in healthcare is already being influenced by predictions or
recommendations made by data-driven machines. Numerous machine learning applications …

Pediatric congenital heart disease (CHD) patients are at higher risk of postoperative
complications and clinical deterioration either due to their underlying pathology or due to the …

Research-based AI-driven systems to predict clinical deterioration are increasingly being
developed, but few are being implemented into clinical workflows. Escobar et al.(AAM) …

The ability to perform accurate prognosis is crucial for proactive clinical decision making,
informed resource management and personalised care. Existing outcome prediction models …