Using Generative Artificial Intelligence to Identify Central Line-associated Bloodstream Infections.
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| Title: | Using Generative Artificial Intelligence to Identify Central Line-associated Bloodstream Infections. |
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| Authors: | Morgan DJ; Medical Care Center, VA Maryland Healthcare System, Veterans Health Administration, U.S. Department of Veterans Affairs, Baltimore, Maryland, USA.; Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, USA., AlShanqeeti S; Medical Care Center, VA Maryland Healthcare System, Veterans Health Administration, U.S. Department of Veterans Affairs, Baltimore, Maryland, USA.; Division of Infectious Disease, University of Maryland School of Medicine, Baltimore, Maryland, USA., Coffey KC; Medical Care Center, VA Maryland Healthcare System, Veterans Health Administration, U.S. Department of Veterans Affairs, Baltimore, Maryland, USA.; Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, USA., Baghdadi JD; Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, USA.; Institute for Healthcare Computing, University of Maryland, North Bethesda, Maryland, USA., Goodman KE; Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, USA.; Institute for Healthcare Computing, University of Maryland, North Bethesda, Maryland, USA., Holman JL; VA Puget Sound Healthcare System, Veterans Health Administration, U.S. Department of Veterans Affairs, Seattle, Washington, USA.; VA National Artificial Intelligence Institute, Digital Health Office, Washington, District of Columbia, USA., Pineles L; Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, USA.; Institute for Healthcare Computing, University of Maryland, North Bethesda, Maryland, USA., Goedken CC; VA National Artificial Intelligence Institute, Digital Health Office, Washington, District of Columbia, USA.; VA Iowa City Health Care, Veterans Health Administration, U.S. Department of Veterans Affairs, Iowa City, Iowa, USA., Firestone C; VA National Artificial Intelligence Institute, Digital Health Office, Washington, District of Columbia, USA., Branch-Elliman W; VA National Artificial Intelligence Institute, Digital Health Office, Washington, District of Columbia, USA.; VA Greater Los Angeles HCxS-West LA, Veterans Health Administration, U.S. Department of Veterans Affairs, Los Angeles, California, USA.; Department of Medicine, UCLA David Geffen School of Medicine, Los Angeles, California, USA. |
| Corporate Authors: | VA AI CLABSI Study Group |
| Source: | Clinical infectious diseases : an official publication of the Infectious Diseases Society of America [Clin Infect Dis] 2026 Apr 30; Vol. 82 (4), pp. e773-e780. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Oxford University Press Country of Publication: United States NLM ID: 9203213 Publication Model: Print Cited Medium: Internet ISSN: 1537-6591 (Electronic) Linking ISSN: 10584838 NLM ISO Abbreviation: Clin Infect Dis Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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