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Machine Learning Insights into Antimicrobial Efficacy and Resistance

Presented at: Microbiology Week Virtual Event Series 2023
C.E. Credits: P.A.C.E. CE ｜ Florida CE
Speaker
  • Jason H. Yang, PhD

    Jason H. Yang, PhD

    Assistant Professor and Chancellor Scholar, Department of Microbiology, Biochemistry and Molecular Genetics, Ruy V. Lourenço Center for Emerging and Re-Emerging Pathogens, Rutgers New Jersey Medical School
    BIOGRAPHY
Abstract

Antimicrobial resistance is an important and pressing challenge for global human health. Several obstacles in understanding, diagnosing, and treating antimicrobial resistance limit our ability to address this growing challenge in the clinic. Here we discuss recent innovations in machine learning that are poised to address each of these obstacles.

Learning Objectives:

1. Recognize mechanisms underlying antimicrobial efficacy and resistance.

2. Identify critical obstacles in addressing the problem of antimicrobial resistance.

3. Define cutting-edge machine learning approaches to diagnosing and treating resistance.

Attendees