Developing a Patient Vocabulary Set using Health Consumer Contributed Content
Developing a patient vocabulary set using health consumer contributed content
Researchers in Drexel’s College of Computing & Informatics have developed a method based on co-occurrence analysis for identifying consumer health expressions from consumer-contributed content, such as from PatientLikeMe, and medical terminology from the National Library of Medicine. The expressions used by patients are quite different from those by healthcare professionals and medical reference sources, and with the exponential increase in the use of consumer-contributed content, there is a wealth of information available online to patients. The developed algorithm identifies and collects frequently used terms by patients to express their symptoms and healthcare concerns. The seed terms are expanded from the extracted data to develop a reference patient vocabulary. Subsequent analysis determines the associations of the patient vocabulary to the technical medical terms.
Applications
- Automatically extract and identify consumer health expressions
- Corpus of consumer health vocabulary
Advantages
- Reduce analysis time from manual monitoring
- Facilitate translation of medical terminology into lay terms
- Improve consumer healthcare decision making
- Reduce language gap between physicians and patients in describing medical concepts
Intellectual Property and Development Status
Copyright
References
Jiang L. and Yang C.C. Expanding Consumer Health Vocabularies by Learning Consumer Health Expressions from Online Health Social Media. Proceedings of International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, 2015.
Jiang L. et al. Discovering Consumer Health Expressions from Consumer-Contributed Content. Proceedings of International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, 2013.