Machine learning for early detection of sepsis: an internal and temporal validation study.
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| Title: | Machine learning for early detection of sepsis: an internal and temporal validation study. |
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| Authors: | Bedoya AD; Department of Medicine, Division of Pulmonary, Allergy, and Critical Care Medicine, Duke University, Durham, North Carolina, USA., Futoma J; Department of Statistics, Duke University, Durham, North Carolina, USA.; John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA., Clement ME; Department of Medicine, Division of Infectious Diseases, Duke University, Durham, North Carolina, USA., Corey K; Duke Institute for Health Innovation, Durham, North Carolina, USA.; Duke University School of Medicine, Durham, North Carolina, USA., Brajer N; Duke Institute for Health Innovation, Durham, North Carolina, USA.; Duke University School of Medicine, Durham, North Carolina, USA., Lin A; Duke Institute for Health Innovation, Durham, North Carolina, USA.; Duke University School of Medicine, Durham, North Carolina, USA., Simons MG; Duke Institute for Health Innovation, Durham, North Carolina, USA.; Duke University School of Medicine, Durham, North Carolina, USA., Gao M; Duke Institute for Health Innovation, Durham, North Carolina, USA., Nichols M; Duke Institute for Health Innovation, Durham, North Carolina, USA., Balu S; Duke Institute for Health Innovation, Durham, North Carolina, USA.; Duke University School of Medicine, Durham, North Carolina, USA., Heller K; Department of Statistics, Duke University, Durham, North Carolina, USA., Sendak M; Duke Institute for Health Innovation, Durham, North Carolina, USA., O'Brien C; Department of Medicine, Durham, North Carolina, USA. |
| Source: | JAMIA open [JAMIA Open] 2020 Apr 11; Vol. 3 (2), pp. 252-260. Date of Electronic Publication: 2020 Apr 11 (Print Publication: 2020). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Oxford University Press on behalf of the American Medical Informatics Association Country of Publication: United States NLM ID: 101730643 Publication Model: eCollection Cited Medium: Internet ISSN: 2574-2531 (Electronic) Linking ISSN: 25742531 NLM ISO Abbreviation: JAMIA Open Subsets: PubMed not MEDLINE |
| Database: | MEDLINE Ultimate |
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