Authors:
Adam Urban
1
;
James Connolly
1
;
Gauri Vaidya
1
;
Krishn Kumar Gupt
2
and
Meghana Kshirsagar
1
Affiliations:
1
Department of Computer Science and Information Systems, University of Limerick, Ireland
;
2
Systems Biology Ireland, School of Medicine, University College Dublin, Ireland
Keyword(s):
Shiny, R Programming, Bioconductor, Exploratory Data Analysis, Visualization, Medical Datasets, Bioinformatics, Data Science.
Abstract:
The extraction of actionable insights from data-driven analyses is crucial for efficiently profiling, analyzing, and visualizing intricate medical datasets. A robust, generic data profiling tool is essential to uncover and understand relationships within medical data. In this context, building Shiny app can lead to numerous advantages—providing interactive, user-friendly, and dynamic dashboards, and the capacity to deploy scalable, web-based solutions. In this article, we introduce DataPulse, a versatile data profiling tool designed to analyze multimodal healthcare data. By leveraging advanced statistical methodologies, DataPulse uncovers complex relationships in the data and displays them through comprehensive dashboards. For instance, in ra-diogenomics, sequential imaging visualizations can highlight dynamic changes in disease progression over time. The article discusses two usecases of DataPulse: one focusing on the analysis of hip fracture patient pathways in the emergency depart
ment, and the other offering a detailed exploration of cancer disease through multimodal datasets to derive insights on drug outcomes and disease progression over time. In conclusion, DataPulse exemplifies how robust, interactive data profiling can transform complex medical data into actionable insights.
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