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Louis H Miller

Biographic Data

ID5417577
NAMELouis H Miller
GIVEN NAMESLouis H
FAMILY NAMEMiller
SIGNATUREMILLER L H
AFFILIATIONSis assistant professor of cardiology and assistant dean for career advisement, Zucker School of Medicine at Hofstra/Northwell, New York, New York.
ORCID0000-0002-3709-0092
VERIFIEDYes
TOTAL WORKS3
TOTAL CITATIONS2
AUTHOR COUNT3
EDITOR COUNT0
FIRST PUBLICATION YEAR1971
LATEST PUBLICATION YEAR2021
H-INDEX1
  • Development and Validation of a Machine Learning-Based Decision Support Tool for Residency Applicant Screening and Review

    Jesse Burk-Rafel, Ilan Reinstein et al.•ARTICLE•Academic Medicine•2021

    PURPOSE: 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. METHOD: Categorical applicant data from the 2018, 2019, and 2020 resid…

  • The spirit of Dakar: A call for action on malaria

    Open Access•Joseph Bruno, Richard Feachem et al.•ARTICLE•Nature•1997

  • Planning a community mental health program: A case history

    Open Access•Harvey M Freed, Louis Miller et al.•ARTICLE•Community Mental Health Journal•1971•Cited by: 2•References: 6

  • Planning a community mental health program: A case history

    Open Access•Harvey M Freed, Louis Miller et al.•ARTICLE•Community Mental Health Journal•1971•Cited by: 2•References: 6

  • Planning a community mental health program: A case history

    Open Access•Harvey M Freed, Louis Miller et al.•ARTICLE•Community Mental Health Journal•1971•Cited by: 2•References: 6

  • The spirit of Dakar: A call for action on malaria

    Open Access•Joseph Bruno, Richard Feachem et al.•ARTICLE•Nature•1997

  • Development and Validation of a Machine Learning-Based Decision Support Tool for Residency Applicant Screening and Review

    Jesse Burk-Rafel, Ilan Reinstein et al.•ARTICLE•Academic Medicine•2021

    PURPOSE: 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. METHOD: Categorical applicant data from the 2018, 2019, and 2020 resid…

Computer Science (2 works) · Medicine (2 works) · Psychology (2 works) · Action (physics) (1 works) · Advertising (1 works) · African history and culture studies (1 works) · Ancient history (1 works) · Artificial Intelligence (1 works) · Artificial Intelligence in Healthcare and Education (1 works) · Biology (1 works)

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