BACKGROUND Utilisation of routinely collected electronic health records from secondary care offers unprecedented possibilities for medical science research but can also present difficulties. One key issue is that medical information is presented as free-form text and, therefore, requires time commitment from clinicians to manually extract salient information. Natural language processing (NLP) methods can be used to automatically extract clinically relevant information. OBJECTIVE Our aim is to use natural language processing (NLP) to capture real-world data on individuals with depression from the Clinical Record Interactive Search (CRIS) clinical text to foster the use of electronic healthcare data in mental health research. METHODS We used a combination of methods to extract salient information from electronic health records. First, clinical experts define the information of interest and subsequently build the training and testing corpora for statistical models. Second, we built and fine-tuned the statistical models using active learning procedures. FINDINGS Results show a high degree of accuracy in the extraction of drug-related information. Contrastingly, a much lower degree of accuracy is demonstrated in relation to auxiliary variables. In combination with state-of-the-art active learning paradigms, the performance of the model increases considerably. CONCLUSIONS This study illustrates the feasibility of using the natural language processing models and proposes a research pipeline to be used for accurately extracting information from electronic health records. CLINICAL IMPLICATIONS Real-world, individual patient data are an invaluable source of information, which can be used to better personalise treatment.

中文翻译：

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使用 UK-CRIS 构建抑郁症临床文本数据的自然语言处理。

背景技术从二级护理中定期收集的电子健康记录的利用为医学科学研究提供了前所未有的可能性，但也可能带来困难。一个关键问题是医疗信息以自由格式文本的形式呈现，因此需要临床医生投入时间来手动提取显着信息。自然语言处理（NLP）方法可用于自动提取临床相关信息。目标我们的目标是使用自然语言处理（NLP）从临床记录交互式搜索（CRIS）临床文本中捕获抑郁症患者的真实数据，以促进电子医疗数据在心理健康研究中的使用。方法 我们结合使用多种方法从电子健康记录中提取显着信息。首先，临床专家定义感兴趣的信息，然后构建统计模型的训练和测试语料库。其次，我们使用主动学习程序构建并微调统计模型。调查结果 结果显示，药物相关信息的提取具有很高的准确性。相比之下，辅助变量的准确度要低得多。与最先进的主动学习范例相结合，模型的性能显着提高。结论本研究说明了使用自然语言处理模型的可行性，并提出了一种用于从电子健康记录中准确提取信息的研究流程。临床意义 真实世界的个体患者数据是宝贵的信息来源，可用于更好地个性化治疗。