Machine Learning and Interactive Visualization to Detect Dose Associated Adverse Drug Events in Drugs Prescribed to Older Adults

In this research project, we aim to address the uncertainties in the efficacy, safety, and dosing of prescription drugs, particularly for older adults who are more likely to take multiple medications. Pre-market drug trials often exclude the older population, leading to potential risks. Our proposed method involves utilizing machine learning and interactive visualization techniques to analyze healthcare databases in Ontario. By simultaneously examining hundreds of drugs in older adults and comparing high-dose versus low-dose effects, we aim to identify potential adverse reactions. By replicating our findings in other regions and translating them into guidelines, we will enhance prescription drug safety and improve patient outcomes.

Faculty Supervisor:

Kamran Sedig

Student:

Partner:

Institute for Clinical Evaluative Sciences (London, ON)

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

The University of Western Ontario

Program: