Pain is a common reason for accessing healthcare resources and is a growing area of research, especially in its overlap with mental health. Mental health electronic health records are a good data source to study this overlap. However, much information on pain is held in the free text of these records, where mentions of pain present a unique natural language processing problem due to its ambiguous nature. This project uses data from an anonymised mental health electronic health records database. The data are used to train a machine learning based classification algorithm to classify sentences as discussing patient pain or not. This will facilitate the extraction of relevant pain information from large databases, and the use of such outputs for further studies on pain and mental health. 1,985 documents were manually triple-annotated for creation of gold standard training data, which was used to train three commonly used classification algorithms. The best performing model achieved an F1-score of 0.98 (95% CI 0.98-0.99).

本研究使用心理健康电子健康记录数据库中的数据训练基于机器学习的分类算法，以从大型数据库中提取相关的疼痛信息，并将其用于进一步研究疼痛和心理健康，并最终通过分类器的优化来实现精度的提高。

自然语言处理方法识别心理健康记录中的疼痛提及