使用自然语言处理识别可能未被诊断的患有莱维体痴呆症的人
Mohamed Heybe1, Lucy Gibson1,2, Annabel C Price3,4
1Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
npj aging
|July 18, 2025
概括
自然语言处理 (NLP) 通过在临床记录中识别痴呆症与莱维体 (DLB) 特征来帮助痴呆症研究. NLP发现,许多阿尔茨海默病 (AD) 患者表现出DLB特征,这表明潜在的误诊.
科学领域:
- 神经学 神经学
- 医疗信息学 医疗信息学
背景情况:
- 临床记录包含了对痴呆症研究有价值的数据.
- 勒维体痴呆症 (DLB) 与阿尔茨海默病 (AD) 有共同的特征,使诊断复杂化.
研究的目的:
- 评估自然语言处理 (NLP) 在电子健康记录中识别DLB的核心特征的实用性.
- 评估在被诊断患有阿尔茨海默病 (AD) 的患者中DLB核心特征的患病率.
主要方法:
- 开发了NLP算法来检测关键的DLB诊断特征.
- 这些算法应用于14,329名患有AD或DLB诊断的患者的大数据集.
主要成果:
- 与AD患者相比,所有核心DLB特征在被诊断为DLB的患者中明显更为普遍.
- 值得注意的是,在被诊断为阿尔茨海默病的患者中,有18.7%的患者表现出两个或两个以上的核心DLB特征.
结论:
- 在从常规临床数据中提取DLB核心特征时,NLP是有效的.
- 很大一部分AD患者可能未被诊断为DLB,这凸显了改善诊断工具的需要.
- NLP可以帮助识别符合可能的DLB标准但缺乏正式诊断的患者.
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