Multiple Regression Analysis and Frequent Itemset Mining of Electronic Medical Records: A Visual Analytics Approach Using VISA_M3R3

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.

Improved Probabilistic Frequent Itemset Analysis Strategy of Learning Behaviors Based on Eclat Framework

“…Different transactions may have the same items, which makes the path of FP tree overlap. The more overlapped, the greater the path compression space, the higher the access efficiency of FP [12][13][14][15]. FP-growth is used to mine frequent itemsets of learning behaviors.…”

Advanced Data Mining of SSD Quality Based on FP-Growth Data Analysis

“…On one hand, FIM is a collection of items that often appear together. Given the past superiority, FIM has an extensive applicational field from the latest researches, such as extracting useful knowledge from event logs [29], using sliding windows capable of extracting tendencies from continuous data flows [30], directing membrane separator development for microbial fuel cells [31], assisting healthcare researchers in identifying medications and medication combinations that associate with a higher risk of acute kidney injury (AKI) using electronic medical records (EMRs) [32], protecting the cloud servers' privacy of datasets from frequency analysis attack [33], and applying the FP-Growth algorithm in determining frequent itemsets in association with data mining to find customer spending habits in buying goods simultaneously [34]. On the other hand, association rules analyze the possible strong relationship between two items.…”

Deep Visual Analytics (DVA): Applications, Challenges and Future Directions

“…However, several recent studies focused on visualization technique for exploring the field of healthcare analysis [3,21,130]. For example, in 2016, Gotz et al [37] reviews several visualization challenges unique to the healthcare discipline by enabling systems that promise to use ever-improving data-driven evidence help doctors to make more precise diagnoses, institutions identify at-risk patients for intervention, clinicians develop more personalized treatment plans.…”

“…Similar to our focus, a survey on VA for DL is presented by Hohman et al [44]. The following are the main points of their survey: (1) why should various aspects of a DL model be visualised?, (2) who uses DL visualisation?, (3) what to visualise in DL?, (4) how to visualise DL?, and (5) when will the visualisation phase take place during the process of developing and training a network? However, our paper looks at DVA categorised by the main tasks and subtask in the various applications, as well as the methods used (CNNVis [78], RNNVis [88], ActiVis [55], DGViz [72], and so on).…”