  Sarcoidosis is a rare inflammatory disease characterized by the formation of
granulomas in various organs. The disease presents diagnostic and treatment
challenges due to its diverse manifestations and unpredictable nature. In this
study, we employed a Large Language Model (LLM) to analyze sarcoidosis-related
discussions on the social media platform Reddit. Our findings underscore the
efficacy of LLMs in accurately identifying sarcoidosis-related content. We
discovered a wide array of symptoms reported by patients, with fatigue, swollen
lymph nodes, and shortness of breath as the most prevalent. Prednisone was the
most prescribed medication, while infliximab showed the highest effectiveness
in improving prognoses. Notably, our analysis revealed disparities in prognosis
based on age and gender, with women and younger patients experiencing good and
polarized outcomes, respectively. Furthermore, unsupervised clustering
identified three distinct patient subgroups (phenotypes) with unique symptom
profiles, prognostic outcomes, and demographic distributions. Finally,
sentiment analysis revealed a moderate negative impact on patients' mental
health post-diagnosis, particularly among women and younger individuals. Our
study represents the first application of LLMs to understand sarcoidosis
through social media data. It contributes to understanding the disease by
providing data-driven insights into its manifestations, treatments, prognoses,
and impact on patients' lives. Our findings have direct implications for
improving personalized treatment strategies and enhancing the quality of care
for individuals living with sarcoidosis.
