您认为是什么导致了您的肌萎缩侧索硬化症?利用人工智能和定性方法对疾病预防控制中心全国肌萎缩侧索硬化症患者登记定性风险因素数据进行分析。

Danielle Boyce, Jaime Raymond, Theodore C Larson, Eddie Kirkland, D Kevin Horton, Paul Mehta
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摘要

目的:肌萎缩性脊髓侧索硬化症(ALS)是一种无法治愈的渐进性神经退行性疾病,对健康造成巨大负担,其病因却鲜为人知。这项分析评估了美国疾病控制和预防中心国家 ALS 登记处的 3061 名参与者在回答 "您认为是什么导致了您的 ALS "这一问题时所作的叙述性回答。方法:数据分析采用定性方法和人工智能(AI),使用自然语言处理(NLP),特别是转换器双向编码器表征(BERT)来探讨参与者对其病因的看法。结果定性分析和人工智能分析方法都揭示了几个通常是一致的主题,这些主题指出了包括遗传、环境和军事接触在内的已知病因。然而,定性分析揭示了详细的主题和次主题,从而更全面地了解了参与者的看法。虽然人工智能和定性分析之间存在一致的地方,但人工智能更广泛的类别并不能捕捉到使用更传统的定性方法所发现的细微差别。定性分析还显示,在描述 ALS 的潜在原因时,有时会出现自责和其他适应不良的应对机制。结论:这项分析强调了 ALS 患者认为导致其疾病的各种因素。了解这些认知有助于临床医生更好地为 ALS 患者(PLWALS)提供支持。该分析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益处。这一快速发展的数据科学领域有可能消除获取 ALS 患者丰富叙述的障碍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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.

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