Multi-morbidity Characterization and Polypharmacy Side Effect Detection for designing Optimal Personalized Healthcare with Machine Learning

Despite a significant improvement in healthcare systems over the past decades, the rapid growth in the number of patients with multiple chronic diseases – called multimorbidity – stands as a complex challenge to healthcare services that are primarily designed to treat individuals with single conditions. Advances in machine learning as well as in computing power now enable us to exploit a vast amount of healthcare data. The main goal of this project is to propose a data-driven approach to characterize patients with multimorbidity in such a way that an optimal care can be given to each of them, using machine learning techniques. The project will use the ICES (Institute for Clinical Evaluative Sciences) dataset, Ontario public health data that is completely anonymized and collected from 1992, containing information on around 15 million Ontario residents. TO BE CONT'D

Intern: 
Seung Eun Yi
Faculty Supervisor: 
Marzyeh Ghassemi
Province: 
Ontario
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