Department of Psychiatry, University of Oxford, Oxford, Oxfordshire, UK
Department of Psychiatry, University of Oxford, Oxford, Oxfordshire, UK.
Evid Based Ment Health. 2020 Feb;23(1):21-26. doi: 10.1136/ebmental-2019-300134.
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.
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.
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.
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.
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.
Real-world, individual patient data are an invaluable source of information, which can be used to better personalise treatment.
利用二级保健中常规收集的电子健康记录为医学科学研究提供了前所未有的可能性，但也带来了困难。一个关键问题是，医疗信息以自由格式的文本呈现，因此需要临床医生投入时间手动提取重要信息。自然语言处理（NLP）方法可用于自动提取临床相关信息。
我们旨在使用自然语言处理（NLP）从临床记录交互搜索（CRIS）临床文本中捕获个体抑郁症的真实世界数据，以促进电子医疗数据在精神健康研究中的应用。
我们使用了多种方法从电子健康记录中提取重要信息。首先，临床专家定义感兴趣的信息，然后为统计模型构建培训和测试语料库。其次，我们使用主动学习程序构建和微调统计模型。
结果表明，在提取药物相关信息方面具有很高的准确性。相比之下，在辅助变量方面的准确性要低得多。与最先进的主动学习范例相结合，模型的性能大大提高。
本研究说明了使用自然语言处理模型的可行性，并提出了一个研究管道，用于从电子健康记录中准确提取信息。
真实世界的个体患者数据是信息的宝贵来源，可以用于更好地进行个体化治疗。
