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This paper presents a nationwide EHR-based chronic rhinosinusitis prediction model using demographic-stratified models and a hybrid feature-selection pipeline, achieving an overall AUC of 0.8461 on data from the All of Us Research Program.
MIT researchers have developed PULSE-HF, a deep learning model that predicts whether heart failure patients will experience worsening left ventricular ejection fraction within a year using electrocardiograms. The model, published in Lancet eClinical Medicine, could help clinicians prioritize high-risk patients and reduce unnecessary hospital visits in both well-resourced and low-resource clinical settings.