Multi-modal machine learning risk models for cardiovascular disease
Principal Investigator: Dr Kenney Ng
Approved Research ID: 50658
Approval date: October 30th 2019
The main goal of this project is to help clinicians better predict the possibility of serious cardiovascular diseases. By working with genomics, clinical data and advanced machine learning methods, we hope this three-year project will help provide doctors with tools to tap into the potential of combined genomics and clinical data, and better understand the possibility an individual has for a certain disease. Equipped with this knowledge, health professionals can potentially intervene and help to reduce this risk or use this information to inform therapy trials. Built for specific health conditions, such as cardiac arrest, these models are designed to identify when a confluence of low incidence and rare genetic events come together and combine with clinical, physiological and environmental factors to form a significant risk factor for a disease. Additionally, these models will learn from various multitudes of disparate data, including the longitudinal and clinical records of individuals, electronic medical records, DNA sequencing and genetic factors. The risk analysis will aim to help health professionals identify and quantify patients' risk for cardiovascular conditions, as well as the genetic factors contributing to that risk. To better improve how this intelligence can be communicated and used in clinical settings, we will work closely together to incorporate direct feedback from doctors and caregivers.