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Development and validation of a machine learning-based decision support tool for residency applicant screening and review

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Año de publicación 2021
Idioma Inglés
Publicado por Academic Medicine

Descripción: Abstract: Residency programs face overwhelming numbers of residency applications, limiting holistic review. Artificial intelligence techniques have been proposed to address this challenge but have not been created. Here, a multidisciplinary team sought to develop and validate a machine learning (ML)-based decision support tool (DST) for residency applicant screening and review. Results The ML model areas under the receiver operating characteristic and precision recall curves were 0.95 and 0.76, respectively; these changed to 0.94 and 0.72, respectively, with removal of USMLE scores. Applicants’ medical school information was an important driver of predictions—which had face validity based on the local selection process—but numerous predictors contributed. Program directors used the DST in the 2021 application cycle to select 20 applicants for interview that had been initially screened out during human review. Conclusions The authors developed and validated an ML algorithm for predicting residency interview offers from numerous application elements with high performance—even when USMLE scores were removed. Model deployment in a DST highlighted its potential for screening candidates and helped quantify and mitigate biases existing in the selection process. Further work will incorporate unstructured textual data through natural language processing methods.
Citación recomendada (normas APA)
: Jesse; Reinstein Peckel Burk-Rafel, "Development and validation of a machine learning-based decision support tool for residency applicant screening and review", Colombia:Academic Medicine, 2021. Consultado en línea en la Biblioteca Digital de Bogotá (https://www.bibliotecadigitaldebogota.gov.co/resources/3711962/), el día 2025-05-03.

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Fuente Biblioteca Virtual Banco de la República
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Año de publicación 2021
Formato PDF
Cobertura Colombia

Development and validation of a machine learning-based decision support tool for residency applicant screening and review

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