Machine Learning to Predict the Likelihood of Acute Myocardial Infarction

Martin Than,John W. Pickering,Yader Sandoval,Anoop S.V. Shah,Athanasios Tsanas,Fred S. Apple,Stefan Blankenberg,Louise Cullen,Christian Mueller,Johannes Tobias Neumann,Raphael Twerenbold,Dirk Westermann,Agim Beshiri,Nicholas L. Mills

Machine Learning to Predict the Likelihood of Acute Myocardial Infarction

2019

Martin Than
John W. Pickering
Yader Sandoval
Anoop S.V. Shah
Athanasios Tsanas
Fred S. Apple
Stefan Blankenberg
Louise Cullen
Christian Mueller
Johannes Tobias Neumann
Raphael Twerenbold
Dirk Westermann
Agim Beshiri
Nicholas L. Mills

Background: 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 approa...

Keywords:

Medicine
Cardiology
Acute coronary syndrome
Myocardial infarction
Internal medicine
Diabetes mellitus
Troponin
in patient
cardiac troponin

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