Selection of 51 predictors from 13,782 candidate multimodal features using machine learning improves coronary artery disease prediction.

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作者： Saaket,Agrawal ^[1] ; Marcus D R,Klarqvist ^[2] ; Connor,Emdin ^[3] ; Aniruddh P,Patel ^[4] ; Manish D,Paranjpe ^[5] ; Patrick T,Ellinor ^[1] ; Anthony,Philippakis ^[2] ; Kenney,Ng ^[3] ; Puneet,Batra ^[4] ; Amit V,Khera ^[1]

作者单位： Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA. ^[1] Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, 185 Cambridge Street, Simches Research Building ^[2] CPZN 6.256, Boston, MA 02114, USA. ^[3] Department of Medicine, Harvard Medical School, Boston, MA, USA. ^[4] Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA. ^[5] Center for Computational Health, IBM Research, Cambridge, MA, USA. ^[6]

关键词 Framingham risk score QRISK3 coronary artery disease machine learning pooled cohort equations risk prediction

DOI 10.1016/j.patter.2021.100364

PMID 34950898

发布时间 2023-11-08

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Patterns (New York, N.Y.)

2021年2卷12期

100364页

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Selection of 51 predictors from 13,782 candidate multimodal features using machine learning improves coronary artery disease prediction.

Patterns (New York, N.Y.)

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Selection of 51 predictors from 13,782 candidate multimodal features using machine learning improves coronary artery disease prediction.

Patterns (New York, N.Y.)

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Patterns (New York, N.Y.)

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