VISEMURE: A Visual Analytics System for Making Sense of Multimorbidity Using Electronic Medical Record Data

Maede S. Nouri, Daniel J. Lizotte, Kamran Sedig and Sheikh S. Abdullah
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Maede S. Nouri: Insight Lab, Western University, London, ON N6A 3K7, Canada
Daniel J. Lizotte: Department of Computer Science, Faculty of Science and Department of Epidemiology and Biostatistics, Western University, London, ON N6A 3K7, Canada
Kamran Sedig: Insight Lab, Western University, London, ON N6A 3K7, Canada
Sheikh S. Abdullah: Insight Lab, Western University, London, ON N6A 3K7, Canada

Data, 2021, vol. 6, issue 8, 1-19

Abstract: Multimorbidity is a growing healthcare problem, especially for aging populations. Traditional single disease-centric approaches are not suitable for multimorbidity, and a holistic framework is required for health research and for enhancing patient care. Patterns of multimorbidity within populations are complex and difficult to communicate with static visualization techniques such as tables and charts. We designed a visual analytics system called VISEMURE that facilitates making sense of data collected from patients with multimorbidity. With VISEMURE, users can interactively create different subsets of electronic medical record data to investigate multimorbidity within different subsets of patients with pre-existing chronic diseases. It also allows the creation of groups of patients based on age, gender, and socioeconomic status for investigation. VISEMURE can use a range of statistical and machine learning techniques and can integrate them seamlessly to compute prevalence and correlation estimates for selected diseases. It presents results using interactive visualizations to help healthcare researchers in making sense of multimorbidity. Using a case study, we demonstrate how VISEMURE can be used to explore the high-dimensional joint distribution of random variables that describes the multimorbidity present in a patient population.

Keywords: multimorbidity; visual analytics; conditional probability; binary logistic regression; softmax regression; decision tree; electronic medical record data (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2021
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