Using Machine Learning Algorithms to Identify Key Predictors of Invasive Mold Infection Surveillance.

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Bibliographic Details
Title: Using Machine Learning Algorithms to Identify Key Predictors of Invasive Mold Infection Surveillance.
Authors: Williams SL; Mycotic Diseases Branch, Centers for Disease Control and Prevention, Atlanta, Georgia, USA., Salah Z; Surveillance, Information Management, and Statistics Office, Centers for Disease Control and Prevention, Atlanta, Georgia, USA., Jackson BR; Mycotic Diseases Branch, Centers for Disease Control and Prevention, Atlanta, Georgia, USA., Wurster S; Division of Internal Medicine, MD Anderson Cancer Center, University of Texas, Houston, Texas, USA., Serpa JA; Section of Infectious Diseases, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA., Grimes CZ; Division of Infectious Diseases, McGovern Medical School, University of Texas, Houston, Texas, USA., Atmar RL; Section of Infectious Diseases, Department of Medicine, Baylor College of Medicine, Houston, Texas, USA., Chiller TM; Mycotic Diseases Branch, Centers for Disease Control and Prevention, Atlanta, Georgia, USA., Kontoyiannis DP; Division of Internal Medicine, MD Anderson Cancer Center, University of Texas, Houston, Texas, USA., Ostrosky-Zeichner L; Division of Infectious Diseases, McGovern Medical School, University of Texas, Houston, Texas, USA., Toda M; Mycotic Diseases Branch, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
Source: The Journal of infectious diseases [J Infect Dis] 2025 Dec 20; Vol. 232 (6), pp. e1033-e1042.
Publication Type: Journal Article
Journal Info: Publisher: Oxford University Press Country of Publication: United States NLM ID: 0413675 Publication Model: Print Cited Medium: Internet ISSN: 1537-6613 (Electronic) Linking ISSN: 00221899 NLM ISO Abbreviation: J Infect Dis Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1537-6613
DOI:10.1093/infdis/jiaf219