VISEMURE: A Visual Analytics System for Making Sense of Multimorbidity Using Electronic Medical Record Data
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Data |
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| Online Access: | https://www.mdpi.com/2306-5729/6/8/85 |
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| author | Maede S. Nouri Daniel J. Lizotte Kamran Sedig Sheikh S. Abdullah |
| author_facet | Maede S. Nouri Daniel J. Lizotte Kamran Sedig Sheikh S. Abdullah |
| author_sort | Maede S. Nouri |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T08:53:40Z |
| format | Article |
| id | doaj.art-7a161eb2befd4cb1b8791f828ab40f5e |
| institution | Directory Open Access Journal |
| issn | 2306-5729 |
| language | English |
| last_indexed | 2024-03-10T08:53:40Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj.art-7a161eb2befd4cb1b8791f828ab40f5e2023-11-22T07:18:14ZengMDPI AGData2306-57292021-08-01688510.3390/data6080085VISEMURE: A Visual Analytics System for Making Sense of Multimorbidity Using Electronic Medical Record DataMaede S. Nouri0Daniel J. Lizotte1Kamran Sedig2Sheikh S. Abdullah3Insight Lab, Western University, London, ON N6A 3K7, CanadaDepartment of Computer Science, Faculty of Science and Department of Epidemiology and Biostatistics, Western University, London, ON N6A 3K7, CanadaInsight Lab, Western University, London, ON N6A 3K7, CanadaInsight Lab, Western University, London, ON N6A 3K7, CanadaMultimorbidity 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.https://www.mdpi.com/2306-5729/6/8/85multimorbidityvisual analyticsconditional probabilitybinary logistic regressionsoftmax regressiondecision tree |
| spellingShingle | Maede S. Nouri Daniel J. Lizotte Kamran Sedig Sheikh S. Abdullah VISEMURE: A Visual Analytics System for Making Sense of Multimorbidity Using Electronic Medical Record Data Data multimorbidity visual analytics conditional probability binary logistic regression softmax regression decision tree |
| title | VISEMURE: A Visual Analytics System for Making Sense of Multimorbidity Using Electronic Medical Record Data |
| title_full | VISEMURE: A Visual Analytics System for Making Sense of Multimorbidity Using Electronic Medical Record Data |
| title_fullStr | VISEMURE: A Visual Analytics System for Making Sense of Multimorbidity Using Electronic Medical Record Data |
| title_full_unstemmed | VISEMURE: A Visual Analytics System for Making Sense of Multimorbidity Using Electronic Medical Record Data |
| title_short | VISEMURE: A Visual Analytics System for Making Sense of Multimorbidity Using Electronic Medical Record Data |
| title_sort | visemure a visual analytics system for making sense of multimorbidity using electronic medical record data |
| topic | multimorbidity visual analytics conditional probability binary logistic regression softmax regression decision tree |
| url | https://www.mdpi.com/2306-5729/6/8/85 |
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