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.

翻译：本研究提出了一种新颖的方法，用于从在线患者体验叙述中对患者情绪进行建模。我们采用元数据网络主题建模分析来自Care Opinion的患者报告体验，揭示了与患者-护理人员互动及临床结果相关的关键情绪主题。我们开发了一个基于概率且考虑上下文的情绪推荐系统，该系统能够利用朴素贝叶斯分类器，以具有上下文意义的主题作为预测因子，同时预测多标签情绪和二元情感。与基线模型相比，该模型预测的情绪在信息检索指标nDCG和Q-measure上表现更优，预测情感的F1得分达到0.921，显著优于标准情感词典。该方法为理解患者反馈提供了一种透明、经济高效的途径，改进了传统数据收集方法，并为个性化患者护理提供依据。我们的研究成果可通过R包和交互式仪表板获取，为医疗研究人员和从业者提供了宝贵工具。