Endometriosis Online Communities: How Machine Learning Can Help Physicians Understand What Patients Are Discussing Online.
Academic Article
Overview
abstract
STUDY OBJECTIVE: Use machine learning to characterize the content of endometriosis online community posts and comments. DESIGN: Retrospective Descriptive Study SETTING: Endometriosis online health communities (OHCs) on the platform Reddit. PARTICIPANTS: Users of the endometriosis online health communities r/Endo and r/endometriosis. INTERVENTIONS: Machine learning was used to analyze thousands of posts made to endometriosis OHCs. Content of posts and comments were interpreted using topic modeling, persona identification and intent labeling. Measurements included baseline characteristics of users, posts and comments to the OHCs. Machine learning techniques; topic modeling, intent labeling and persona identification were used to identify the most common topics of conversation, the intents behind the posts and the subjects of people discussed in posts. System performance was assessed via accuracy at F1 score. RESULTS: A total of 34,715 posts and 353,162 comments responding to posts were evaluated. The topics most likely to be a subject of a post were menstruation (8%), sharing symptoms (8%), medical appointments (8%), medical story (9%), and empathy (7%). The majority of posts were written with the intent of seeking information about endometriosis (49%) or seeking the experiences of others with endometriosis (29%). Users expressed a strong preference for surgeons performing excision rather than ablation of endometriosis. CONCLUSION: Endometriosis OHCs are mostly used to learn about symptoms of endometriosis and share one's medical experiences. Posts and comments from users highlight the need for more empathy in the clinical care of endometriosis and easier access for patients to high quality information about endometriosis.
