Open AccessMachine 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 Müeller,
Johannes Neumann,
Raphael Twerenbold,
Dirk Westermann,
Agim Beshiri,
Nicholas L Mills,
MI .Michigan Value Collaborative
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 , area under the curve , percentile , predictive value of tests , myocardial infarction diagnosis , likelihood ratios in diagnostic testing , infarction , troponin , machine learning , 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.