Open AccessPrediction of cardiac death after adenosine myocardial perfusion SPECT based on machine learning
Author(s) -
David Haro Alonso,
Miles N. Wernick,
Yongyi Yang,
Guido Germano,
Daniel S. Berman,
Piotr J. Slomka
Publication year - 2019
Publication title -
journal of nuclear cardiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.791
H-Index - 82
eISSN - 1532-6551
pISSN - 1071-3581
DOI - 10.1007/s12350-018-1250-7
Subject(s) - medicine , lasso (programming language) , logistic regression , artificial intelligence , support vector machine , machine learning , area under the curve , receiver operating characteristic , perfusion scanning , area under curve , cardiology , perfusion , computer science , pharmacokinetics , world wide web
We developed machine-learning (ML) models to estimate a patient's risk of cardiac death based on adenosine myocardial perfusion SPECT (MPS) and associated clinical data, and compared their performance to baseline logistic regression (LR). We demonstrated an approach to visually convey the reasoning behind a patient's risk to provide insight to clinicians beyond that of a "black box."