A richly interactive exploratory data analysis and visualization tool using electronic medical records

Abstract

BackgroundElectronic medical records (EMRs) contain vast amounts of data that is of great interest to physicians, clinical researchers, and medial policy makers. As the size, complexity, and accessibility of EMRs grow, the ability to extract meaningful information from them has become an increasingly important problem to solve.MethodsWe develop a standardized data analysis process to support cohort study with a focus on a particular disease. We use an interactive divide-and-conquer approach to classify patients into relatively uniform within each group. It is a repetitive process enabling the user to divide the data into homogeneous subsets that can be visually examined, compared, and refined. The final visualization was driven by the transformed data, and user feedback direct to the corresponding operators which completed the repetitive process. The output results are shown in a Sankey diagram-style timeline, which is a particular kind of flow diagram for showing factors’ states and transitions over time.ResultsThis paper presented a visually rich, interactive web-based application, which could enable researchers to study any cohorts over time by using EMR data. The resulting visualizations help uncover hidden information in the data, compare differences between patient groups, determine critical factors that influence a particular disease, and help direct further analyses. We introduced and demonstrated this tool by using EMRs of 14,567 Chronic Kidney Disease (CKD) patients.ConclusionsWe developed a visual mining system to support exploratory data analysis of multi-dimensional categorical EMR data. By using CKD as a model of disease, it was assembled by automated correlational analysis and human-curated visual evaluation. The visualization methods such as Sankey diagram can reveal useful knowledge about the particular disease cohort and the trajectories of the disease over time.

Yield and bias in defining a cohort study baseline from electronic health record data

“…[22, 23, 32, 33] Pivovarov and colleagues used histograms to examine laboratory testing dynamics; in doing so, they identified multimodality in testing patterns associated with, for example, inpatient versus outpatient status. [29] Baseline measurements of cholesterol and BP are central to CVD prediction in the virtual cohort study we are creating.…”

Dynamic visualization of multi-level molecular data: The Director package in R

“…While visualizations succeed in making data more appealing and approachable to a general audience, the addition of dynamic elements further engages users to explore and interact with the data. Users thus gain a more intuitive understanding of https://doi.org/10.1016/j.cmpb.2017.10.013 0169-2607/© 2017 Published by Elsevier Ireland Ltd. the data and, consequently, improved discovery of biomedical insights [7][8][9] .…”

BioSankey: Visualizing microbial communities and gene expression data over time

“…Sankey diagrams are also commonly used in other research areas to highlight changes over time, e.g. in eye dynamics (Burch et al 2013), medical records (Huang et al 2015), energy flows in cities (Chen & Chen 2017), energy efficiency (Dietmair & Verl 2009) and voter transition (Fieldhouse & Prosser 2016).…”