Electronic Phenotyping of Urinary Tract Infections as a Silver Standard Label for Machine Learning.

Ma, Stephen P; Hosgur, Ebru; Corbin, Conor K; Lopez, Ivan; Chang, Amy; Chen, Jonathan H

Ma, Stephen P; Hosgur, Ebru; Corbin, Conor K; Lopez, Ivan; Chang, Amy; Chen, Jonathan H.

Afiliación

Ma SP; Stanford University School of Medicine, Stanford, CA, USA.
Hosgur E; Stanford University School of Medicine, Stanford, CA, USA.
Corbin CK; Stanford University School of Medicine, Stanford, CA, USA.
Lopez I; Stanford University School of Medicine, Stanford, CA, USA.
Chang A; Stanford University School of Medicine, Stanford, CA, USA.
Chen JH; Stanford University School of Medicine, Stanford, CA, USA.

AMIA Jt Summits Transl Sci Proc ; 2024: 182-189, 2024.

Article en En | MEDLINE | ID: mdl-38827068

ABSTRACT

ABSTRACT

This study explored the efficacy of electronic phenotyping in data labeling for machine learning with a focus on urinary tract infections (UTIs). We contrasted labels from electronic phenotyping against previously published labels such as urine culture positivity. In comparison, electronic phenotyping showed the potential to enhance specificity in UTI labeling while maintaining similar sensitivity and was easily scaled for application to a large dataset suitable for machine learning, which we used to train and validate a machine learning model. Electronic phenotyping offers a valuable method for machine learning label generation in healthcare, with potential benefits for patient care and antimicrobial stewardship. Further research will expand its application and optimize techniques for increased performance.

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: AMIA Jt Summits Transl Sci Proc Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos

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