Detecting QT prolongation from a single-lead ECG with deep learning.
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| Title: | Detecting QT prolongation from a single-lead ECG with deep learning. |
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| Authors: | Alam R; Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.; Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America., Aguirre A; Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America.; Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, United States of America.; Harvard Medical School, Boston, Massachusetts, United States of America.; Harvard-MIT Program in Health Sciences and Technology, Cambridge, Massachusetts, United States of America., Stultz CM; Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.; Computer Science & Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.; Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, United States of America.; Harvard-MIT Program in Health Sciences and Technology, Cambridge, Massachusetts, United States of America.; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America. |
| Source: | PLOS digital health [PLOS Digit Health] 2024 Jun 25; Vol. 3 (6), pp. e0000539. Date of Electronic Publication: 2024 Jun 25 (Print Publication: 2024). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: PLOS Country of Publication: United States NLM ID: 9918335064206676 Publication Model: eCollection Cited Medium: Internet ISSN: 2767-3170 (Electronic) Linking ISSN: 27673170 NLM ISO Abbreviation: PLOS Digit Health Subsets: PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
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