Heybe Mohamed, Gibson Lucy, Price Annabel C, Cardinal Rudolf N, O'Brien John T, Stewart Robert, Mueller Christoph
Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
South London and Maudsley NHS Foundation Trust, London, UK.
NPJ Aging. 2025 Jul 18;11(1):68. doi: 10.1038/s41514-025-00252-x.
Natural language processing (NLP) can expand the utility of clinical records data in dementia research. We deployed NLP algorithms to detect core features of dementia with Lewy bodies (DLB) and applied those to a large database of patients diagnosed with dementia in Alzheimer's disease (AD) or DLB. Of 14,329 patients identified, 4.3% had a diagnosis of DLB and 95.7% of dementia in AD. All core features were significantly commoner in DLB than in dementia in AD, although 18.7% of patients with dementia in AD had two or more DLB core features. In conclusion, NLP applications can identify core features of DLB in routinely collected data. Nearly one in five patients with dementia in AD have two or more DLB core features and potentially qualify for a diagnosis of probable DLB. NLP may be helpful to identify patients who may fulfil criteria for DLB but have not yet been diagnosed.
自然语言处理（NLP）可以拓展临床记录数据在痴呆症研究中的效用。我们部署了NLP算法来检测路易体痴呆（DLB）的核心特征，并将其应用于一个诊断为阿尔茨海默病（AD）或DLB的大型患者数据库。在识别出的14329名患者中，4.3%被诊断为DLB，95.7%为AD痴呆症。所有核心特征在DLB中比在AD痴呆症中显著更常见，尽管18.7%的AD痴呆症患者有两个或更多DLB核心特征。总之，NLP应用可以在常规收集的数据中识别DLB的核心特征。近五分之一的AD痴呆症患者有两个或更多DLB核心特征，可能符合疑似DLB的诊断标准。NLP可能有助于识别那些可能符合DLB标准但尚未被诊断的患者。
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