Predicting multifaceted risks using machine learning in atrial fibrillation: insights from GLORIA-AF study

Juan Lu; Arnaud Bisson; Mohammed Bennamoun; Yalin Zheng; Frank Sanfilippo; Joseph Hung; Tom Briffa; B. McQuillan; Jonathon Stewart; Gemma A. Figtree; Menno V. Huisman; Girish Dwivedi; Professor Gregory Lip

doi:10.1093/ehjdh/ztae010

Abstract

1 min read

The ML-GBDT model outperformed clinical risk scores in predicting the risks in patients with AF. This approach could be used as a single multifaceted holistic tool to optimize patient risk assessment and mitigate adverse outcomes when managing AF.

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