PAGE TITLE
Developing a patient vocabulary set using health consumer contributed content
PAGE SUMMARY
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
TITLE: Applications
Automatically extract and identify consumer health expressions
Corpus of consumer health vocabulary
ADVANTAGES
TITLE: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
IP STATUS
Intellectual Property and Development Status
Copyright
PUBLICATIONS
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.
CONTACT INFORMATION
Tanvi Muni
Licensing Manager
Drexel University
tm3439@drexel.edu