Using Machine Learning and the Electronic Health Record to Predict Complicated Clostridium difficile Infection | Zendy

Benjamin Y. Li | Zendy; Jeeheh Oh | Zendy; Vincent B. Young | Zendy; Krishna Rao | Zendy; Jenna Wiens | Zendy

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Using Machine Learning and the Electronic Health Record to Predict Complicated Clostridium difficile Infection

Author(s) -

Benjamin Y. Li,

Jeeheh Oh,

Vincent B. Young,

Krishna Rao,

Jenna Wiens

Publication year - 2019

Publication title -

open forum infectious diseases

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.546

H-Index - 35

ISSN - 2328-8957

DOI - 10.1093/ofid/ofz186

Subject(s) - clostridium difficile , medicine , electronic health record , c difficile , clostridium , health records , artificial intelligence , microbiology and biotechnology , intensive care medicine , health care , antibiotics , bacteria , computer science , economics , biology , economic growth , genetics

Using EHR data, we can accurately stratify CDI cases according to their risk of developing complications. Such an approach could be used to guide future clinical studies investigating interventions that could prevent or mitigate complicated CDI.

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