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Text mining and natural language processing approaches for automatic categorization of lay requests to web-based
Wolfgang Himmel1, Ulrich Reincke, Hans Wilhelm Michelmann
1Department of General Practice/Family Medicine, University of Göttingen, Humboldtallee 38, 37070 Göttingen, Germany. whimmel@gwdg.de
Journal of Medical Internet Research
|July 28, 2009
Summary
This study developed an automated method to classify online medical questions, achieving high accuracy for many categories. This helps doctors manage patient inquiries more efficiently.
Area of Science:
- Computational linguistics
- Medical informatics
- Natural Language Processing
Background:
- Growing use of electronic media for health information seeking.
- Internet users increasingly utilize "ask the doctor" services.
Purpose of the Study:
- To automatically classify lay requests to an Internet medical expert forum.
- Employ a combination of text-mining strategies for classification.
Main Methods:
- Manual classification of 988 requests into 38 categories.
- Utilized Cramer's V, Principal Component Analysis, and Singular Value Decomposition.
- Trained regression models to predict category probabilities.
Main Results:
- Top subject matter categories: In Vitro Fertilization (IVF), ovulation, cycle, semen analysis.
- High prevalence of "general information" (54%) and "treatment recommendations" (36%) in expectations.
- Achieved 100% precision and recall in 47% of categories; >80% in 92%.
Conclusions:
- Developed an automated approach for classifying unstructured information in online medical forums.
- Enables preliminary categorization of new requests.
- Aids medical experts in managing information and providing feedback.
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