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Diagnostic suspicion bias and machine learning: Breaking the awareness deadlock for sepsis detection.
Prasad, Varesh; Aydemir, Baturay; Kehoe, Iain E; Kotturesh, Chaya; O'Connell, Abigail; Biebelberg, Brett; Wang, Yang; Lynch, James C; Pepino, Jeremy A; Filbin, Michael R; Heldt, Thomas; Reisner, Andrew T.
Affiliation
  • Prasad V; Harvard-MIT Program in Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
  • Aydemir B; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
  • Kehoe IE; Department of Emergency Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America.
  • Kotturesh C; Department of Emergency Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America.
  • O'Connell A; Department of Emergency Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America.
  • Biebelberg B; Department of Emergency Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America.
  • Wang Y; Department of Emergency Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America.
  • Lynch JC; Department of Emergency Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America.
  • Pepino JA; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
  • Filbin MR; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
  • Heldt T; Department of Emergency Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America.
  • Reisner AT; Department of Emergency Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America.
PLOS Digit Health ; 2(11): e0000365, 2023 Nov.
Article in En | MEDLINE | ID: mdl-37910497
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Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: PLOS Digit Health Year: 2023 Document type: Article Affiliation country: United States Country of publication: United States
Fulltext
Add to My VHL
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Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: PLOS Digit Health Year: 2023 Document type: Article Affiliation country: United States Country of publication: United States