A new AI-enhanced ECG model, AIRE, accurately predicts mortality and heart disease risk, providing clinicians with actionable, patient-specific insights.
By Hugo Francisco de SouzaReviewed by Susha Cheriyedath, M.Sc.Nov 5 2024 Developed using data from diverse patient groups, AIRE’s advanced AI predicts heart disease risk and mortality with precision, giving clinicians tools for more targeted, long-term patient care.
Study findings revealed that the novel model can accurately predict all-cause mortality, ventricular arrhythmia, atherosclerotic CVD, and heart failure risk. Recent advances in computer processing capabilities and the advent of next-generation predictive machine learning models have sparked excitement in the research community.
“Existing mortality prediction models are limited by predicting survival at one or a small number of set timepoints and do not provide information on specific actionable pathways. A high-risk prediction is unhelpful to a clinician if there is no accompanying information on how to improve the survival trajectory of their patient.
AIRE’s unique approach uses patient-specific survival curves, allowing it to predict "time-to-mortality" rather than just providing set time-point risks. Additionally, five other models focusing on CV death , non-CV death , atherosclerotic cardiovascular disease , ventricular arrhythmia , and heart failure were derived using similar approaches.
Notably, AIRE was capable of accurately predicting heart failure events in participants without a personal or family history of CVD, which is especially relevant as conventional diagnoses in these populations are typically delayed.
Heart Heart Disease Arrhythmia Artificial Intelligence Cardiovascular Disease Diagnostic Heart Failure Imaging Mortality Research
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