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.
View Full Project DescriptionKamran Sedig
Institute for Clinical Evaluative Sciences (London, ON)
Computer science
Professional, scientific and technical services
The University of Western Ontario
Elevate
