Multiple Regression Analysis and Frequent Itemset Mining of Electronic Medical Records: A Visual Analytics Approach Using VISA_M3R3 Article Swipe
Sheikh S. Abdullah
,
Neda Rostamzadeh
,
Kamran Sedig
,
Amit X. Garg
,
Eric McArthur
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/data5020033
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/data5020033
Medication-induced acute kidney injury (AKI) is a well-known problem in clinical medicine. This paper reports the first development of a visual analytics (VA) system that examines how different medications associate with AKI. In this paper, we introduce and describe VISA_M3R3, a VA system designed to assist healthcare researchers in identifying medications and medication combinations that associate with a higher risk of AKI using electronic medical records (EMRs). By integrating multiple regression models, frequent itemset mining, data visualization, and human-data interaction mechanisms, VISA_M3R3 allows users to explore complex relationships between medications and AKI in such a way that would be difficult or sometimes even impossible without the help of a VA system. Through an analysis of 595 medications using VISA_M3R3, we have identified 55 AKI-inducing medications, 24,212 frequent medication groups, and 78 medication groups that are associated with AKI. The purpose of this paper is to demonstrate the usefulness of VISA_M3R3 in the investigation of medication-induced AKI in particular and other clinical problems in general. Furthermore, this research highlights what needs to be considered in the future when designing VA systems that are intended to support gaining novel and deep insights into massive existing EMRs.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/data5020033
- https://www.mdpi.com/2306-5729/5/2/33/pdf?version=1585818002
- OA Status
- gold
- Cited By
- 16
- References
- 102
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3013273263
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3013273263Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/data5020033Digital Object Identifier
- Title
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Multiple Regression Analysis and Frequent Itemset Mining of Electronic Medical Records: A Visual Analytics Approach Using VISA_M3R3Work title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-03-29Full publication date if available
- Authors
-
Sheikh S. Abdullah, Neda Rostamzadeh, Kamran Sedig, Amit X. Garg, Eric McArthurList of authors in order
- Landing page
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https://doi.org/10.3390/data5020033Publisher landing page
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https://www.mdpi.com/2306-5729/5/2/33/pdf?version=1585818002Direct link to full text PDF
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2306-5729/5/2/33/pdf?version=1585818002Direct OA link when available
- Concepts
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Visual analytics, Medical record, Visualization, Analytics, Health records, Data science, Acute kidney injury, Computer science, Electronic medical record, Medicine, Medical emergency, Data mining, Health care, Internal medicine, Economics, Economic growthTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
16Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 2, 2023: 1, 2022: 1, 2021: 7Per-year citation counts (last 5 years)
- References (count)
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102Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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