Abstract
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.
Highlights
As part of modernizing their operations, healthcare and medical organizations are adopting electronic medical records (EMRs) and deploying new information technology systems that generate, collect, digitize, and analyze their data [1]
The purpose of this paper is to demonstrate how visual analytics (VA) systems can be designed in a systematic way: (1) to examine the association between medications and acute kidney injury (AKI), in particular, and (2) to support other clinical investigations involving EMRs, in general
The purpose of this paper is to demonstrate how VA systems can be designed in a systematic way to support EMR-driven tasks and investigation of different clinical problems
Summary
As part of modernizing their operations, healthcare and medical organizations are adopting electronic medical records (EMRs) and deploying new information technology systems that generate, collect, digitize, and analyze their data [1]. With the development of EMRs and the extensive use of computerized provider order entry tools, patients’ medication profile data is accessible and processable for secondary reuses [2,3]. A common problem in clinical medicine which may lead to development of acute kidney injury (AKI) is medication-induced nephrotoxicity [14,15,16]. VA systems fuse the strengths of automated analysis and interactive visualizations to allow users to explore data interactively, identify patterns, apply filters, and manipulate data to achieve their goals. This process is more complicated than an automated internal analysis coupled with an external visualization to show the results. To understand the concepts of VA, we discuss the spatial structure and different modules of VA systems
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