Objective: Amyotrophic lateral sclerosis (ALS) is an incurable, progressive neurodegenerative disease with a significant health burden and poorly understood etiology. This analysis assessed the narrative responses from 3,061 participants in the Centers for Disease Control and Prevention's National ALS Registry who answered the question, "What do you think caused your ALS?" Methods: Data analysis used qualitative methods and artificial intelligence (AI) using natural language processing (NLP), specifically, Bidirectional Encoder Representations from Transformers (BERT) to explore responses regarding participants' perceptions of the cause of their disease. Results: Both qualitative and AI analysis methods revealed several, often aligned themes, which pointed to perceived causes including genetic, environmental, and military exposures. However, the qualitative analysis revealed detailed themes and subthemes, providing a more comprehensive understanding of participants' perceptions. Although there were areas of alignment between AI and qualitative analysis, AI's broader categories did not capture the nuances discovered using the more traditional, qualitative approach. The qualitative analysis also revealed that the potential causes of ALS were described within narratives that sometimes indicate self-blame and other maladaptive coping mechanisms. Conclusions: This analysis highlights the diverse range of factors that individuals with ALS consider as perceived causes for their disease. Understanding these perceptions can help clinicians to better support people living with ALS (PLWALS). The analysis highlights the benefits of using traditional qualitative methods to supplement or improve upon AI-based approaches. This rapidly evolving area of data science has the potential to remove barriers to accessing the rich narratives of people with lived experience.
Keywords: Amyotrophic lateral sclerosis; artificial intelligence; lived experience; natural language processing; qualitative study; real world evidence.