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Machine learning to predict bacteriologic confirmation of Mycobacterium tuberculosis in infants and very young children.

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Title: Machine learning to predict bacteriologic confirmation of Mycobacterium tuberculosis in infants and very young children.
Authors: Smith JP; Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut, United States of America.; Division of Global HIV and Tuberculosis, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America., Milligan K; Division of Global HIV and Tuberculosis, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.; Peraton, Atlanta, Georgia, United States of America., McCarthy KD; Division of Global HIV and Tuberculosis, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America., Mchembere W; Center for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya., Okeyo E; Center for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya., Musau SK; Center for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya., Okumu A; Center for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya., Song R; Oxford Vaccine Group, Department of Paediatrics, University of Oxford, Oxford, United Kingdom.; Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, United States of America., Click ES; Division of Global HIV and Tuberculosis, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America., Cain KP; Division of Global HIV and Tuberculosis, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America.
Source: PLOS digital health [PLOS Digit Health] 2023 May 17; Vol. 2 (5), pp. e0000249. Date of Electronic Publication: 2023 May 17 (Print Publication: 2023).
Publication Type: Journal Article
Journal Info: Publisher: PLOS Country of Publication: United States NLM ID: 9918335064206676 Publication Model: eCollection Cited Medium: Internet ISSN: 2767-3170 (Electronic) Linking ISSN: 27673170 NLM ISO Abbreviation: PLOS Digit Health Subsets: PubMed not MEDLINE
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