使用自然语言处理从阿尔茨海默病患者临床笔记中提取睡眠信息
Extraction of Sleep Information from Clinical Notes of Patients with Alzheimer's Disease Using Natural Language Processing
AI总结：
本研究利用自然语言处理技术从阿尔茨海默病患者临床笔记中自动提取睡眠信息，开发了基于规则、机器学习和LLM的方法，其中基于规则的算法在F1和PPV上表现最佳，为大规模睡眠-AD关联研究提供了高效工具。
摘要：
阿尔茨海默病（AD）是美国最常见的痴呆症形式。睡眠已被证明是影响老年认知功能的关键生活方式相关因素之一。然而，目前缺乏研究睡眠与AD发病率之间关联的研究。开展此类研究的一个主要瓶颈是，传统获取睡眠信息的方式耗时、低效、不可扩展，且仅限于患者的主观体验。我们从adSLEEP中随机抽取了570份临床笔记文档进行人工标注，创建了一个金标准数据集。adSLEEP是一个包含从匹兹堡大学医学中心（UPMC）检索到的7,266名AD患者的192,000份去标识化临床笔记的语料库。我们开发了基于规则的NLP算法、机器学习模型以及基于大语言模型（LLM）的NLP算法，以自动从金标准数据集中提取睡眠相关概念，包括打鼾、小睡、睡眠问题、睡眠质量差、白天嗜睡、夜间醒来和睡眠时长。基于规则的NLP算法在所有睡眠相关概念上取得了最佳的F1分数。在阳性预测值（PPV）方面，基于规则的NLP算法在白天嗜睡和睡眠时长上达到1.00；机器学习模型在小睡上达到0.95，在睡眠质量差上达到0.86，在打鼾上达到0.90；经过微调的LLAMA2在夜间醒来上达到0.93，在睡眠问题上达到0.89，在睡眠时长上达到1.00。结果表明，基于规则的NLP算法在所有睡眠概念上始终取得最佳性能。本研究聚焦于AD患者的临床笔记，但可扩展到其他疾病的通用睡眠信息提取。
英文摘要：
Alzheimer's Disease (AD) is the most common form of dementia in the United States. Sleep is one of the lifestyle-related factors that has been shown critical for optimal cognitive function in old age. However, there is a lack of research studying the association between sleep and AD incidence. A major bottleneck for conducting such research is that the traditional way to acquire sleep information is time-consuming, inefficient, non-scalable, and limited to patients' subjective experience. A gold standard dataset is created from manual annotation of 570 randomly sampled clinical note documents from the adSLEEP, a corpus of 192,000 de-identified clinical notes of 7,266 AD patients retrieved from the University of Pittsburgh Medical Center (UPMC). We developed a rule-based Natural Language Processing (NLP) algorithm, machine learning models, and Large Language Model(LLM)-based NLP algorithms to automate the extraction of sleep-related concepts, including snoring, napping, sleep problem, bad sleep quality, daytime sleepiness, night wakings, and sleep duration, from the gold standard dataset. Rule-based NLP algorithm achieved the best performance of F1 across all sleep-related concepts. In terms of Positive Predictive Value (PPV), rule-based NLP algorithm achieved 1.00 for daytime sleepiness and sleep duration, machine learning models: 0.95 and for napping, 0.86 for bad sleep quality and 0.90 for snoring; and LLAMA2 with finetuning achieved PPV of 0.93 for Night Wakings, 0.89 for sleep problem, and 1.00 for sleep duration. The results show that the rule-based NLP algorithm consistently achieved the best performance for all sleep concepts. This study focused on the clinical notes of patients with AD, but could be extended to general sleep information extraction for other diseases.
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