(Discovery) Big Data Analytics in Large AF Patient Databases: Correlates to Hospitalizations and ED Visits

CANet > CANet Newsroom > (Discovery) Big Data Analytics in Large AF Patient Databases: Correlates to Hospitalizations and ED Visits

Project Lead: Dr. Jiro Inoue, Post-doctoral Fellow, Western University
Mentor: Dr. Maria Drangova (Western University)
Amount: $10,000 (CANet)
The use of big data analytics (BDA) in cardiovascular care is in its infancy – while the potential value of BDA is high, so far there is little evidence of direct impact on patient care. One possible reason for this is that most applications of BDA of medical data take predictive machine learning approaches. These methods attempt to predict outcomes or prescribe treatments for disease models that are already well understood. The impact of these approaches will be in augmenting clinical workflows rather than in generating new knowledge. Instead, this project proposes to generate new knowledge through BDA by using clustering approaches to identify new correlations, patterns, and anomalies in the data that have not yet been identified and generate hypotheses that can explain the findings.

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