Patient contrastive learning: A performant, expressive, and practical approach to electrocardiogram modeling.
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| Title: | Patient contrastive learning: A performant, expressive, and practical approach to electrocardiogram modeling. |
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| Authors: | Diamant N; Research Laboratory of Electronics, MIT, Cambridge, Massachusetts, United States of America.; Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America., Reinertsen E; Research Laboratory of Electronics, MIT, Cambridge, Massachusetts, United States of America.; Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, United States of America., Song S; Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, United States of America., Aguirre AD; Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, United States of America.; Center for Systems Biology, Massachusetts General Hospital Research Institute and Harvard Medical School, Boston, Massachusetts, United States of America.; Wellman Center for Photomedicine, Massachusetts General Hospital Research Institute and Harvard Medical School, Boston, Massachusetts, United States of America., Stultz CM; Research Laboratory of Electronics, MIT, Cambridge, Massachusetts, United States of America.; Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, United States of America.; Department of Electrical Engineering and Computer Science, MIT, Cambridge, Massachusetts, United States of America.; Harvard-MIT Division of Health Sciences and Technology, Cambridge, Massachusetts, United States of America., Batra P; Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, United States of America. |
| Source: | PLoS computational biology [PLoS Comput Biol] 2022 Feb 14; Vol. 18 (2), pp. e1009862. Date of Electronic Publication: 2022 Feb 14 (Print Publication: 2022). |
| Publication Type: | Journal Article; Research Support, Non-U.S. Gov't |
| Journal Info: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101238922 Publication Model: eCollection Cited Medium: Internet ISSN: 1553-7358 (Electronic) Linking ISSN: 1553734X NLM ISO Abbreviation: PLoS Comput Biol Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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