Endometriosis Online Communities: How Machine Learning Can Help Physicians Understand What Patients Are Discussing...

Area of Science:

• Digital Health
• Computational Linguistics
• Women's Health

Background:

• Online health communities (OHCs) provide platforms for patients to share experiences and seek information.
• Endometriosis is a chronic condition affecting many women, with significant impact on quality of life.
• Understanding patient-generated content in OHCs is crucial for improving care and support.

Purpose of the Study:

• To characterize the content of posts and comments within endometriosis online communities using machine learning.
• To identify prevalent topics, user intents, and discussion subjects within these digital spaces.

Main Methods:

• Retrospective descriptive study analyzing posts and comments from Reddit endometriosis communities (r/Endo, r/endometriosis).
• Machine learning techniques including topic modeling, persona identification, and intent labeling were employed.
• System performance was evaluated using accuracy at F1-score.

Main Results:

• Analysis of 34,715 posts and 353,162 comments revealed key topics: menstruation, symptom sharing, medical appointments, medical stories, and empathy.
• The primary intents were seeking information (49%) and experiences (29%) related to endometriosis.
• A strong user preference for excision over ablation surgical methods was observed.

Conclusions:

• Endometriosis OHCs serve as vital resources for symptom learning and sharing medical journeys.
• User discussions underscore a need for increased empathy in clinical endometriosis care.
• There is a clear demand for improved patient access to reliable, high-quality endometriosis information.