Machine Learning to Predict the Likelihood of Acute Myocardial Infarction | Zendy

Martin Than | Zendy; John W. Pickering | Zendy; Yader Sandoval | Zendy; Anoop Shah | Zendy; Athanasios Tsanas | Zendy; Fred S. Apple | Zendy; Stefan Blankenberg | Zendy; Louise Cullen | Zendy; Christian Mueller | Zendy; Franz–Josef Neumann | Zendy; Raphael Twerenbold | Zendy; Dirk Westermann | Zendy; Agim Beshiri | Zendy; Nicholas L. Mills | Zendy; Peter M. George | Zendy; Mark Richards | Zendy; Richard W. Troughton | Zendy; Sally Aldous | Zendy; Andrew R. Chapman | Zendy; Atul Anand | Zendy; Jaimi Greenslade | Zendy; William Parsonage | Zendy; Jasper Boeddinghaus | Zendy; Karin Wildi | Zendy; Thomas Nestelberger | Zendy; Patrick Badertscher | Zendy; Shaoqing Du | Zendy; Janel Huang | Zendy; Stephen W. Smith | Zendy; Nils A. Sörensen | Zendy; Francisco Ojeda | Zendy

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Machine Learning to Predict the Likelihood of Acute Myocardial Infarction

Author(s) -

Martin Than,

John W. Pickering,

Yader Sandoval,

Anoop Shah,

Athanasios Tsanas,

Fred S. Apple,

Stefan Blankenberg,

Louise Cullen,

Christian Mueller,

Franz–Josef Neumann,

Raphael Twerenbold,

Dirk Westermann,

Agim Beshiri,

Nicholas L. Mills,

Peter M. George,

Mark Richards,

Richard W. Troughton,

Sally Aldous,

Andrew R. Chapman,

Atul Anand,

Jaimi Greenslade,

William Parsonage,

Jasper Boeddinghaus,

Karin Wildi,

Thomas Nestelberger,

Patrick Badertscher,

Shaoqing Du,

Janel Huang,

Stephen W. Smith,

Nils A. Sörensen,

Francisco Ojeda

Publication year - 2019

Publication title -

circulation

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 7.795

H-Index - 607

eISSN - 1524-4539

pISSN - 0009-7322

DOI - 10.1161/circulationaha.119.041980

Subject(s) - medicine , myocardial infarction , receiver operating characteristic , cardiology , percentile , area under the curve , test set , predictive value of tests , myocardial infarction diagnosis , likelihood ratios in diagnostic testing , troponin , infarction , machine learning , artificial intelligence , statistics , mathematics , computer science

Variations in cardiac troponin concentrations by age, sex, and time between samples in patients with suspected myocardial infarction are not currently accounted for in diagnostic approaches. We aimed to combine these variables through machine learning to improve the assessment of risk for individual patients.

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