VISEMURE: A Visual Analytics System for Making Sense of Multimorbidity Using Electronic Medical Record Data
| Autor: | Kamran Sedig, Daniel J. Lizotte, Sheikh S. Abdullah, Maede Sadat Nouri |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Visual analytics
Information Systems and Management multimorbidity Computer science electronic medical record data media_common.quotation_subject Decision tree binary logistic regression visual analytics Bibliography. Library science. Information resources 03 medical and health sciences 0302 clinical medicine Joint probability distribution Health care decision tree Multimorbidity 030212 general & internal medicine Socioeconomic status media_common Creative visualization business.industry 030503 health policy & services Electronic medical record softmax regression Data science Computer Science Applications conditional probability 0305 other medical science business Information Systems |
| Zdroj: | Data Volume 6 Issue 8 Data, Vol 6, Iss 85, p 85 (2021) |
| ISSN: | 2306-5729 |
| DOI: | 10.3390/data6080085 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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