Using Machine learning and the electronic health record to predict complicated clostridium difficile infection

Open Forum Infectious Diseases

Volumn 6, Issue 5, 2019, Pages

  Li, Benjamin Y ^a   Oh, Jeeheh ^a   Young, Vincent B ^b   Rao, Krishna ^b   Wiens, Jenna ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

^b UNIVERSITY OF MICHIGAN MEDICAL SCHOOL   (United States)

Author keywords

Clostridium (Clostridioides) difficile infection; Complications; Electronic health records; Machine learning; Patient risk stratification

Indexed keywords

METRONIDAZOLE; SODIUM CHLORIDE; VANCOMYCIN;

ADULT; AGED; AREA UNDER THE CURVE; ARTICLE; CLOSTRIDIUM DIFFICILE INFECTION; COHORT ANALYSIS; COLON RESECTION; ELECTRONIC HEALTH RECORD; FEMALE; HUMAN; INTENSIVE CARE UNIT; MACHINE LEARNING; MAJOR CLINICAL STUDY; METHICILLIN RESISTANT STAPHYLOCOCCUS AUREUS; MIDDLE AGED; MORTALITY; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; VANCOMYCIN RESISTANT ENTEROCOCCUS;

EID: 85067183658     PISSN: None     EISSN: 23288957     Source Type: Journal    
DOI: 10.1093/ofid/ofz186     Document Type: Article

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