| Theme: | Theme - Digital Health (Theme - DHLTH), Activity - Collaboratory (Activity - C) |
| Status: | Active |
| Start Date: | 2021-11-24 |
| End Date: | 2021-11-24 |
| Website: | |
| Lead |
| Charlebois, Daniel |
Project Overview
Healthcare professionals must quickly identify pathogens to initiate prompt antimicrobial therapy to patients and to contain their spread within the hospital and community at large. However, drug-resistant pathogens can be notoriously difficult and time consuming to identify using traditional diagnostic tests. We are developing machine learning models to detect drug resistance in fungal pathogens from patient samples. These machine learning models are anticipated to detect drug-resistant pathogens more rapidly and accurately than the currently available diagnostic methods. The ultimate goal is to use machine learning to more effectively treat patients with drug-resistant infections and to better control the spread of infectious diseases.
