Using Generative Artificial Intelligence to Identify Central Line-associated Bloodstream Infections.

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Bibliographic Details
Title: Using Generative Artificial Intelligence to Identify Central Line-associated Bloodstream Infections.
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
Description
ISSN:1537-6591
DOI:10.1093/cid/ciaf636