Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Heart Disorders and Di seases - Atrial Fibrillation is the subject of a report. According to news repor ting originating from Florence, Italy, by NewsRx correspondents, research stated , “The accuracy of available prediction tools for clinical outcomes in patients with atrial fibrillation (AF) remains modest. Machine Learning (ML) has been use d to predict outcomes in the AF population, but not in a population entirely on anticoagulant therapy.” Our news editors obtained a quote from the research from Santa Maria Nuova Hospi tal, “Different supervised ML models were applied to predict all-cause death, ca rdiovascular (CV) death, major bleeding and stroke in anticoagulated patients wi th AF, processing data from the multicenter START-2 Register. 11078 AF patients (male n = 6029, 54.3%) were enrolled with a median follow-up period of 1.5 years [IQR 1.0-2.6]. Patients on V itamin K Antagonists (VKA) were 5135 (46.4%) and 5943 (53.6% ) were on Direct Oral Anticoagulants (DOAC). Using Multi-Gate Mixture of Experts , a cross-validated AUC of 0.779 ± 0.016 and 0.745 ± 0.022 were obtained, respec tively, for the prediction of all-cause death and CV-death in the overall popula tion. The best ML model outperformed CHADSVASC and HAS-BLED for all-cause death prediction (p <0.001 for both). When compared to HAS-BLED, Gradient Boosting improved major bleeding prediction in DOACs patients (0.711 v s. 0.586, p<0.001). A very low number of events during fol low-up (52) resulted in a suboptimal ischemic stroke prediction (best AUC of 0.6 06 ± 0.117 in overall population). Body mass index, age, renal function, platele t count and hemoglobin levels resulted the most important variables for ML predi ction.”
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