Center for Quantitative Methods and Data Science, Institute for Clinical Research and Health Policy Studies, Tufts University School of Medicine, Boston, MA, USA.
Johns Hopkins University Schools of Medicine, Baltimore, MD, USA.
Amyotroph Lateral Scler Frontotemporal Degener. 2024 Aug;25(5-6):615-624. doi: 10.1080/21678421.2024.2349920. Epub 2024 May 8.
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?"
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.
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.
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.
肌萎缩侧索硬化症(ALS)是一种无法治愈的进行性神经退行性疾病,给健康带来了巨大负担,但其病因仍不清楚。本分析评估了疾病控制与预防中心国家 ALS 登记处的 3061 名参与者对问题“您认为是什么导致了您的 ALS?”的叙述性回答。
数据分析采用了定性方法和人工智能(AI),使用自然语言处理(NLP),特别是基于变压器的双向编码器表示(BERT),以探讨参与者对疾病病因的看法。
定性和 AI 分析方法都揭示了几个主题,这些主题往往是一致的,指向了遗传、环境和军事暴露等被认为是疾病的原因。然而,定性分析揭示了更详细的主题和子主题,提供了对参与者看法的更全面理解。尽管 AI 和定性分析之间存在一致性的领域,但 AI 的更广泛类别没有捕捉到使用更传统的定性方法发现的细微差别。定性分析还表明,ALS 的潜在原因是在叙述中描述的,这些叙述有时表明自责和其他适应不良的应对机制。
本分析强调了 ALS 患者认为是其疾病病因的因素的多样性。了解这些看法可以帮助临床医生更好地支持肌萎缩侧索硬化症患者(PLWALS)。分析还强调了使用传统定性方法来补充或改进基于人工智能的方法的好处。这一快速发展的数据科学领域有可能消除获取有生活经验者丰富叙述的障碍。