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What Do You Think Caused Your ALS? An Analysis of the CDC National Amyotrophic Lateral Sclerosis Patient Registry Qualitative Risk Factor Data Using Artificial Intelligence and Qualitative Methodology
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8 2024
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Source: Amyotroph Lateral Scler Frontotemporal Degener. 25(5-6):615-624
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Alternative Title:Amyotroph Lateral Scler Frontotemporal Degener
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Description: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 grounded theory qualitative methods and artificial intelligence (AI) using natural language processing (NLP), specifically, Bidirectional Encoder Representations from Transformers (BERT) to explore the participants responses regarding their perceptions for the “cause” of their disease.
Results:
Both qualitative and AI analysis methods revealed several, often aligned clusters or themes, which pointed to perceived causes such as genetic, environmental, and military exposures. However, the qualitative analysis revealed detailed themes and subthemes, providing a more comprehensive understanding of perceived causes. 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 also sometimes indicated 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 combining qualitative and AI-based approaches in analyzing narrative data. This rapidly evolving area of data science has the potential to remove barriers to accessing the rich narratives of people with lived experience.
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Pubmed ID:38717430
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Pubmed Central ID:PMC11299519
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