AI Model Developed by University of Buffalo Predicts Cardiac Readmissions With 98% Accuracy
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Contributed by: Kate Gamble
Summary
Arinze Nkemdirim Okere, a clinical professor at the University at Buffalo, has developed a machine-learning model capable of predicting hospital readmissions among cardiac patients with a 95% accuracy rate, as detailed in a recent publication in the *British Medical Journal Health and Care Informatics*. This model leverages patient-reported behavioral data alongside electronic health records to identify individuals at higher risk for re-hospitalization, emphasizing the critical role of medication adherence. By analyzing data from over 1,300 participants, the research identifies key factors such as heart disease, medication complexity, and socioeconomic status affecting readmission rates. This advance in AI technology underscores its potential to enhance patient outcomes and mitigate healthcare costs, signaling a significant step forward for healthcare professionals in addressing readmission challenges.
