在9/11后的退伍军人中使用自然语言处理识别前性痴呆症的临床表型
Samin Panahi1,2, Jamie Mayo1,2, Eamonn Kennedy1,2
1VA Salt Lake City Health Care System, Informatics, Decision-Enhancement and Analytic Sciences Center, Salt Lake City, UT, United States.
Frontiers in neurology
|March 1, 2024
概括
退伍军人的前性痴呆症 (FTD) 呈现出不同的症状严重程度和并发症. 使用NLP识别不同的FTD表型有助于早期检测和个性化护理策略.
科学领域:
- 神经退行性疾病 神经退行性疾病
- 退伍军人健康 退伍军人的健康
- 计算语言学 计算语言学
背景情况:
- 前性痴呆症 (FTD) 是一种多样化的神经退行性疾病.
- 有限的研究量化了9/11后退伍军人的FTD表型.
- 对这一群体来说,了解独特的演示文稿至关重要.
研究的目的:
- 在9/11后的退伍军人中确定FTD的独特临床表型.
- 使用自然语言处理 (NLP) 进行医疗病历审查.
- 在退伍军人卫生管理局 (VA) 系统中描述FTD的表现.
主要方法:
- 进行了临床医生笔记的医疗记录图表审查.
- 使用NLP工具提取认知功能障碍特征.
- 将特征组织成研究领域标准 (RDoC) 领域,用于聚类类型.
主要成果:
- 确定了三种FTD症状表型:低 (16.4%),中等 (69.2%) 和高 (14.5%) 的痛苦.
- 高危情绪表型与头部受伤,失眠,心脏问题,焦虑和滥用酒精有关.
- 一个独特的表型表现出结合的语言和行为症状.
结论:
- FTD呈现出严重程度和症状困扰的连续性.
- 增加的症状困扰与更多同时发生的疾病相关.
- 以NLP驱动的表型可能会增强退伍军人早期FTD检测策略.
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