Understanding the patient perspective of epilepsy treatment through text mining of online patient support groups

Abstract

Objective: Epilepsy is among the most common chronic neurologic diseases. There is a need for more data on patient perspectives of treatment to guide patient-centered care initiatives. Patients with epilepsy share their experiences on social media anonymously, but little is known about those discussions. Our aim was to learn what patients with epilepsy discuss regarding their condition and identify treatment-related themes from online patient support groups. Methods: A total of 355,838 posts were collected from three online support groups for patients with epilepsy through a crawling script, and an analytical pipeline was built to identify patient conversation content through leveraging of multiple text mining methods. Results were also displayed by network visualization methods. Results: Patients with epilepsy sought information about medical treatments, shared their treatment experiences, and sought help from other posters. Key themes related to treatments included the search for optimal personalized treatment strategies as well as identifying and coping with adverse effects. Significance: This study showed the feasibility of learning about concerns of patients with epilepsy, especially treatment issues, through text mining methods. However, some manual selection and filtering were necessary to ensure quality results for the treatment analysis. Providers should be aware of online discussions and use analyses of such discussions to help guide effective patient engagement during care.