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Patients were randomly allocated to independent training and testing cohorts in a 70:30 ratio, repeated K=10-fold. Support vector machines and convolutional neural networks were trained to 2 end points: (1) sustained VT\/VF or (2) mortality at 3 years. Support vector machines provided superior classification. For patient-level predictions, we computed personalized MAP scores as the proportion of MAP beats predicting each end point. Patient-level predictions in independent test cohorts yielded c-statistics of 0.90 for sustained VT\/VF (95% CI, 0.76\u20131.00) and 0.91 for mortality (95% CI, 0.83\u20131.00) and were the most significant multivariate predictors. Interpreting trained support vector machine revealed MAP morphologies that, using in silico modeling, revealed higher L-type calcium current or sodium-calcium exchanger as predominant phenotypes for VT\/VF.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions:<\/jats:title>\n            <jats:p>Machine learning of action potential recordings in patients revealed novel phenotypes for long-term outcomes in ischemic cardiomyopathy. Such computational phenotypes provide an approach which may reveal cellular mechanisms for clinical outcomes and could be applied to other conditions.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1161\/circresaha.120.317345","type":"journal-article","created":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T10:00:22Z","timestamp":1605002422000},"page":"172-184","update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":52,"title":["Machine Learned Cellular Phenotypes in Cardiomyopathy Predict Sudden Death"],"prefix":"10.1161","volume":"128","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6585-534X","authenticated-orcid":true,"given":"Albert J.","family":"Rogers","sequence":"first","affiliation":[{"name":"Department of Medicine and Cardiovascular Institute (A.J.R., A.S., M.I.A., T.B., P.C., P.J.W., S.M.N.), Stanford 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