Using large language models to investigate patients' and caregivers' perceptions on SUDEP: A case study with an...

Sudden Unexpected Death in Epilepsy (SUDEP) is a leading cause of epilepsy-related mortality, yet remains under-communicated in clinical practice. Social Media Listening (SML) is a novel method using natural language processing and machine learning to retrieve real-world data. This study uses SML and explores patient and caregiver reports surrounding SUDEP on topics such as information provision, emotional impact, and preventive behavior.

A retrospective observational study was conducted using Artificial Intelligence (AI) and Natural Language Processing (NLP)-powered patient-centricity solutions across online communities from 09/2020-11/2024. Posts from 23,584 authors were analyzed, with 1,381 posts by 789 individuals explicitly mentioning SUDEP. Patient and caregiver narratives were annotated, semantically tagged, analyzed quantitatively and qualitatively using Pharos Analytics^TM and PatientGPT.

Most patients and caregivers reported learning about SUDEP through independent online research, often years after diagnosis, triggering emotions such as fear, shock, frustration. Lack of counseling by healthcare professionals was linked to feelings of betrayal and mistrust. Knowledge about SUDEP promoted adherence to medication, lifestyle adjustments, and use of preventive tools (e.g., seizure alarms, anti-suffocation pillows). Patients emphasized the need for early, transparent SUDEP discussions, ideally at diagnosis of epilepsy; caregivers focused on monitoring and care burden. Counseling was not associated with long-term psychological harm and welcomed for its empowering potential.

Despite clinicians' reluctance to discuss SUDEP due to fear of increasing patient anxiety, this study shows that knowledge about SUDEP may lead to proactive risk management without long-term emotional harm. Early, honest communication is desired by patients and caregivers and vital for implementing preventive strategies.