Machine learning algorithms for predicting urinary tract infections: Integration of demographic data and dipstick reflectance results

Description

This webinar will present how machine learning algorithms can assist clinicians in diagnosing UTIs using instruments already available in the laboratory. It will also introduce a framework for applying machine learning to other clinical applications.

Read the article in Clinical Chemistry

Target audience

This activity is designed for physicians, lab supervisors, lab directors (and/or assistant directors), lab managers (supervisory and/or non-supervisory), point-of-care coordinators, pathologists, toxicologists, fellows, residents, in-training individuals, and other laboratory professionals oversseing/conducting within this topic.

Learning objectives

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

• Understand how machine learning algorithms can support the clinical decision for antibiotic prescription in the context of UTI.
• Understand how to perform a machine learning study for a binary outcome.

Faculty

Moderator

Eric Kilpatrick, MD, FRCPath, FRCPEd
Consultant in Chemical Pathology
Manchester University
NHS Foundation Trust
Manchester, UK

Speaker

Julien Favresse, PhD, PharmD, EuSpLM
Invited Professor
University of Namur
Clinique Saint-Luc Bouge Namur, Belgium

Disclosures and statement of independence

The Association for Diagnostics & Laboratory Medicine (formerly AACC) is dedicated to ensuring balance, independence, objectivity, and scientific rigor in all educational activities. All participating planning committee members and faculty are required to disclose to the program audience any financial relationships related to the subject matter of this program. Disclosure information is reviewed in advance in order to manage and resolve any possible conflicts of interest. The intent of this disclosure is to provide participants with information on which they can make their own judgments.

The following faculty reported no financial relationships:

• Eric Kilpatrick, MD, FRCPath, FRCPEd
• Julien Favresse, PhD, PharmD, EuSpLM

Disclosures and statement of independence

The Association for Diagnostics & Laboratory Medicine (formerly AACC) is dedicated to ensuring balance, independence, objectivity, and scientific rigor in all educational activities. All participating planning committee members and faculty are required to disclose to the program audience any financial relationships related to the subject matter of this program. Disclosure information is reviewed in advance in order to manage and resolve any possible conflicts of interest. The intent of this disclosure is to provide participants with information on which they can make their own judgments.

Content validity

All recommendations involving clinical medicine are based on evidence accepted within the profession of medicine as adequate justification for their indications and contraindications in the care of patients; AND/OR all scientific research referred to or reported in support or justification of a patient care recommendation conforms to generally accepted standards of experimental design, data collection, and analysis.

Accreditation statement

This activity will be submitted for 1.0  ACCENT continuing education credits.

Successful completion statement

Verification of Participation certificates are provided to registered participants based on completion of the activity, in its entirety, and the activity evaluation. For questions regarding continuing education, please email [email protected].