This study introduces a novel methodology for modelling patient emotions from online patient experience narratives. We employed metadata network topic modelling to analyse patient-reported experiences from Care Opinion, revealing key emotional themes linked to patient-caregiver interactions and clinical outcomes. We develop a probabilistic, context-specific emotion recommender system capable of predicting both multilabel emotions and binary sentiments using a naive Bayes classifier using contextually meaningful topics as predictors. The superior performance of our predicted emotions under this model compared to baseline models was assessed using the information retrieval metrics nDCG and Q-measure, and our predicted sentiments achieved an F1 score of 0.921, significantly outperforming standard sentiment lexicons. This method offers a transparent, cost-effective way to understand patient feedback, enhancing traditional collection methods and informing individualised patient care. Our findings are accessible via an R package and interactive dashboard, providing valuable tools for healthcare researchers and practitioners.

该研究介绍了一种从在线患者经验叙述中建模患者情绪的新方法。我们采用元数据网络主题建模分析了来自Care Opinion的患者报告的经验，揭示与患者-医护人员互动和临床结果相关的关键情绪主题。我们开发了一种概率上下文特定的情绪推荐系统，可以使用朴素贝叶斯分类器基于语境有意义的主题来预测多标签情绪和二进制情绪。我们使用信息检索度量指标nDCG和Q-measure评估了本模型下我们预测的情绪的卓越性能，而我们预测的情绪达到了0.921的F1得分，明显优于标准情绪词典。该方法提供了一种透明、经济高效的方式来理解患者反馈，增强了传统的收集方法，并为个性化患者护理提供了信息。我们的研究结果可以通过R包和交互式仪表板获得，为医疗保健研究人员和从业者提供了有价值的工具。

患者报告体验的概率情感和观点建模