Recently natural language processing (NLP) tools have been developed to identify and extract salient risk indicators in electronic health records (EHRs). Sentiment analysis, although widely used in non-medical areas for improving decision making, has been studied minimally in the clinical setting. In this study, we undertook, to our knowledge, the first domain adaptation of sentiment analysis to psychiatric EHRs by defining psychiatric clinical sentiment, performing an annotation project, and evaluating multiple sentence-level sentiment machine learning (ML) models. Results indicate that off-the-shelf sentiment analysis tools fail in identifying clinically positive or negative polarity, and that the definition of clinical sentiment that we provide is learnable with relatively small amounts of training data. This project is an initial step towards further refining sentiment analysis methods for clinical use. Our long-term objective is to incorporate the results of this project as part of a machine learning model that predicts inpatient readmission risk. We hope that this work will initiate a discussion concerning domain adaptation of sentiment analysis to the clinical setting.

本研究首次将情感分析引入精神科领域，通过定义心理临床情感、标注和评估多个机器学习模型，得出结论：现有的情感分析工具不能准确识别临床正面或负面极性，而我们提供的临床情感定义可以用相对较少的训练数据进行学习。该项目是进一步改进临床使用情感分析方法的初步步骤，最终目标是将结果纳入机器学习模型以预测住院患者的再次入院风险。我们希望这项工作会引发有关情感分析领域适应临床应用的讨论。

区分临床情绪：精神科患者健康记录中领域适应的重要性