3. Machine Learning and Modeling in Infectious Diseases
Wednesday, October 3, 2018: 1:30 PM-3:15 PM
Room: S 214-216

Learning Objectives:

At the conclusion of this session, participants will be able to:

  • review the data on how the electronic medical record can be used to generate models that predict risk of healthcare-associated infections.
  • apply machine learning approaches to stratify patients who present with clinical manifestations of sepsis.
  • discuss the impact data-driven approaches can have in the management of patients in the intensive care unit.

Target Audience: Academicians, Clinicians, Epidemiologists, Fellows, Hospital epidemiologists, Infection preventionists, Infectious diseases pediatricians, Infectious diseases physicians, Investigators, Lab personnel, Medical students and residents, Members-in-training, Microbiologists, Pharmacists, Researchers, Scientists

Tracks: Pediatric ID, Trainee, Epidemiology and Infection Control, Investigative ID, Adult ID

Moderators:  Vincent B. Young, MD, PhD, FIDSA, University of Michigan and Erica Shenoy, MD, PhD, Massachusetts General Hospital

CME Credits: Maximum of 1.75 hours of AMA PRA Category 1 Credit™

ACPE Credits: ACPE 1.75 knowledge-based contact hours of pharmacy CE

ACPE Number: 0221-9999-18-181-L01-P


V. B. Young, None

E. Shenoy, None

See more of: Symposium

Findings in the abstracts are embargoed until 12:01 a.m. PDT, Wednesday Oct. 3rd with the exception of research findings presented at the IDWeek press conferences.