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Development of a Systematic Approach to Consultation Record Data from Cancer Consultation and Support Centers
Miwa Mitoma1, Masumi Azuma2, Kyoko Ishigaki3
1School of Nursing, Hyogo University of Health Sciences 1.
Studies in Health Technology and Informatics
|December 18, 2021
Summary
Text mining effectively analyzed cancer support consultations from 2009-2018. This approach revealed consultation patterns and trends over time, demonstrating its utility with growing datasets.
Area of Science:
- Health Informatics
- Data Science in Oncology
Background:
- Cancer support centers accumulate vast consultation records.
- Analyzing these records manually is challenging due to data volume.
Purpose of the Study:
- To apply text mining for feature extraction and structuring of consultation data.
- To analyze temporal changes in consultation content from 2009 to 2018.
Main Methods:
- Utilized text mining techniques to process consultation records.
- Employed co-occurrence network analysis to identify patterns and trends.
Main Results:
- The text-mining approach proved effective for large and expanding datasets.
- Co-occurrence network analysis successfully visualized consultation content patterns over a decade.
Conclusions:
- Text mining is a viable method for analyzing longitudinal cancer consultation data.
- This approach offers insights into evolving patient needs and support trends.
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