GraphXplore: Visual exploration and accessible preprocessing of medical data
Data-driven medical research requires explainable, robust models that can handle the noisy, high-dimensional nature of electronic healthcare data while adequately communicating the results to physicians. Additionally, metadata sharing, and reproducible dataset preparation are needed to increase data...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-02-01
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| Series: | SoftwareX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352711024003480 |
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| Summary: | Data-driven medical research requires explainable, robust models that can handle the noisy, high-dimensional nature of electronic healthcare data while adequately communicating the results to physicians. Additionally, metadata sharing, and reproducible dataset preparation are needed to increase data quality and interoperability of privacy-sensitive patient data. In this work, we present GraphXplore, a tool for visual data exploration, automatic metadata extraction and data transformation. It enables explainable, easy-to-use exploratory data analysis paired with dataset preparation and metadata annotation accessible for physicians. The tool is implemented as a Python package and graphical user interface application tailored to the needs of medical researchers and modularly integrable into hospital data warehouse infrastructures. |
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| ISSN: | 2352-7110 |