Applications of Machine Learning to the Problem of Antimicrobial Resistance: an Emerging Model for Translational Research-Reference-Cited by-同舟云学术

Applications of Machine Learning to the Problem of Antimicrobial Resistance: an Emerging Model for Translational Research

Published:2021-06-18 Issue:7 Volume:59 Page:
ISSN:0095-1137
Container-title:Journal of Clinical Microbiology
language:en
Short-container-title:J Clin Microbiol

Author:

Anahtar Melis N.¹^ORCID,Yang Jason H.²³^ORCID,Kanjilal Sanjat⁴⁵^ORCID

Affiliation:

1. Department of Pathology, Massachusetts General Hospital, Boston, Massachusetts, USA

2. Center for Emerging Pathogens, New Jersey Medical School, Rutgers University, Newark, New Jersey, USA

3. Department of Microbiology, Biochemistry and Molecular Genetics, New Jersey Medical School, Rutgers University, Newark, New Jersey, USA

4. Department of Population Medicine, Harvard Medical School & Harvard Pilgrim Healthcare Institute, Boston, Massachusetts, USA

5. Division of Infectious Diseases, Brigham & Women’s Hospital, Boston, Massachusetts, USA

Abstract

Antimicrobial resistance (AMR) remains one of the most challenging phenomena of modern medicine. Machine learning (ML) is a subfield of artificial intelligence that focuses on the development of algorithms that learn how to accurately predict outcome variables using large sets of predictor variables that are typically not hand selected and are minimally curated.

Funder

HHS | NIH | National Institute of Allergy and Infectious Diseases

HHS | NIH | National Institute of General Medical Sciences

Harvard Catalyst

Publisher

American Society for Microbiology

Subject

Microbiology (medical)

Link

https://journals.asm.org/doi/pdf/10.1128/JCM.01260-20

Reference86 articles.

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