Multiple Regression Analysis and Frequent Itemset Mining of Electronic Medical Records: A Visual Analytics Approach Using VISA_M3R3
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 t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Data |
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| Online Access: | https://www.mdpi.com/2306-5729/5/2/33 |
| _version_ | 1797626383442116608 |
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| author | Sheikh S. Abdullah Neda Rostamzadeh Kamran Sedig Amit X. Garg Eric McArthur |
| author_facet | Sheikh S. Abdullah Neda Rostamzadeh Kamran Sedig Amit X. Garg Eric McArthur |
| author_sort | Sheikh S. Abdullah |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T10:09:38Z |
| format | Article |
| id | doaj.art-2e11fb966a5d4f5d8ea003636cddee5b |
| institution | Directory Open Access Journal |
| issn | 2306-5729 |
| language | English |
| last_indexed | 2024-03-11T10:09:38Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj.art-2e11fb966a5d4f5d8ea003636cddee5b2023-11-16T14:36:13ZengMDPI AGData2306-57292020-03-01523310.3390/data5020033Multiple Regression Analysis and Frequent Itemset Mining of Electronic Medical Records: A Visual Analytics Approach Using VISA_M3R3Sheikh S. Abdullah0Neda Rostamzadeh1Kamran Sedig2Amit X. Garg3Eric McArthur4Insight Lab, Western University, London, ON N6A 3K7, CanadaInsight Lab, Western University, London, ON N6A 3K7, CanadaInsight Lab, Western University, London, ON N6A 3K7, CanadaDepartment of Medicine, Epidemiology and Biostatistics, Western University, London, ON N6A 3K7, CanadaICES, London, ON N6A 3K7, CanadaMedication-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.https://www.mdpi.com/2306-5729/5/2/33visual analyticsmultivariable regressionfrequent itemset mininginteractive visualizationmedication-associated acute kidney injuryelectronic medical records |
| spellingShingle | Sheikh S. Abdullah Neda Rostamzadeh Kamran Sedig Amit X. Garg Eric McArthur Multiple Regression Analysis and Frequent Itemset Mining of Electronic Medical Records: A Visual Analytics Approach Using VISA_M3R3 Data visual analytics multivariable regression frequent itemset mining interactive visualization medication-associated acute kidney injury electronic medical records |
| title | Multiple Regression Analysis and Frequent Itemset Mining of Electronic Medical Records: A Visual Analytics Approach Using VISA_M3R3 |
| title_full | Multiple Regression Analysis and Frequent Itemset Mining of Electronic Medical Records: A Visual Analytics Approach Using VISA_M3R3 |
| title_fullStr | Multiple Regression Analysis and Frequent Itemset Mining of Electronic Medical Records: A Visual Analytics Approach Using VISA_M3R3 |
| title_full_unstemmed | Multiple Regression Analysis and Frequent Itemset Mining of Electronic Medical Records: A Visual Analytics Approach Using VISA_M3R3 |
| title_short | Multiple Regression Analysis and Frequent Itemset Mining of Electronic Medical Records: A Visual Analytics Approach Using VISA_M3R3 |
| title_sort | multiple regression analysis and frequent itemset mining of electronic medical records a visual analytics approach using visa m3r3 |
| topic | visual analytics multivariable regression frequent itemset mining interactive visualization medication-associated acute kidney injury electronic medical records |
| url | https://www.mdpi.com/2306-5729/5/2/33 |
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