Users’ Concerns About Endometriosis on Social Media: Sentiment Analysis and Topic Modeling Study

Background Endometriosis is a chronic gynecological condition that affects a significant portion of women of reproductive age, leading to debilitating symptoms such as chronic pelvic pain and infertility. Despite advancements in diagnosis and management, patient education remains a critical challenge. With the rapid growth of digital platforms, artificial intelligence (AI) has emerged as a potential tool to enhance patient education and access to information. Objective This systematic review aims to explore the role of AI in facilitating education and improving information accessibility for individuals with endometriosis. Methods This review followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines to ensure rigorous and transparent reporting. We conducted a comprehensive search of PubMed; Embase; the Regional Online Information System for Scientific Journals of Latin America, the Caribbean, Spain and Portugal (LATINDEX); Latin American and Caribbean Literature in Health Sciences (LILACS); Institute of Electrical and Electronics Engineers (IEEE) Xplore, and the Cochrane Central Register of Controlled Trials using the terms “endometriosis” and “artificial intelligence.” Studies were selected based on their focus on AI applications in patient education or information dissemination regarding endometriosis. We included studies that evaluated AI-driven tools for assessing patient knowledge and addressed frequently asked questions related to endometriosis. Data extraction and quality assessment were conducted independently by 2 authors, with discrepancies resolved through consensus. Results Out of 400 initial search results, 11 studies met the inclusion criteria and were fully reviewed. We ultimately included 3 studies, 1 of which was an abstract. The studies examined the use of AI models, such as ChatGPT (OpenAI), machine learning, and natural language processing, in providing educational resources and answering common questions about endometriosis. The findings indicated that AI tools, particularly large language models, offer accurate responses to frequently asked questions with varying degrees of sufficiency across different categories. AI’s integration with social media platforms also highlights its potential to identify patients’ needs and enhance information dissemination. Conclusions AI holds promise in advancing patient education and information access for endometriosis, providing accurate and comprehensive answers to common queries, and facilitating a better understanding of the condition. However, challenges remain in ensuring ethical use, equitable access, and maintaining accuracy across diverse patient populations. Future research should focus on developing standardized approaches for evaluating AI’s impact on patient education and exploring its integration into clinical practice to enhance support for individuals with endometriosis.

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Exploring Inflammatory Bowel Disease Discourse on Reddit throughout the COVID-19 Pandemic using OpenAI’s GPT 3.5 Turbo Model: Classification Model Validation and Case Study (Preprint)

Babinski,

Karley,

Cooper

et al. 2024

Preprint

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BACKGROUND Inflammatory bowel disease (IBD) is a chronic autoimmune disorder with an increasing prevalence. Online communities have become vital for communication among IBD patients, especially throughout the COVID-19 pandemic. However, these interactions remain largely underexplored. OBJECTIVE This study aims to analyze community posts from three of the largest IBD support groups on Reddit between March 1, 2020, and December 31, 2022, using a pre-trained transformer model, and to validate the classification system's results via comparison to human scoring. METHODS We collected 53,333 posts and classified them using OpenAI's GPT-3.5 Turbo model to determine sentiment, categorize topics, and identify demographic information and COVID-19 mentions. Manual validation was performed on a subset of 397 posts to measure inter-rater agreement between human raters and the GPT-3.5 model. RESULTS Fleiss’ kappa and Gwet’s AC1 coefficients indicated a high level of agreement between raters, with values ranging from 0.53 to 0.91. Medication (n = 14,909) and Symptoms (n = 14,939) emerged as the most discussed topics. Most posts conveyed a neutral sentiment. While most users did not disclose their age, those who did primarily fell into the 20-29 (n = 2,392) and 30-39 (n = 859) age ranges. After an initial spike in posts within the first month, most posts did not reference the COVID-19 pandemic. CONCLUSIONS Our study showcases the potential of generative pre-trained transformer models in processing and extracting insights from medical social media data. Future research can benefit from further sub-analyses of our validated dataset or utilize OpenAI’s model to analyze social media data for other conditions, particularly those where patient experiences are challenging to collect.

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