患者报告体验的概率情感与情绪建模
Probabilistic emotion and sentiment modelling of patient-reported experiences
AI总结：
本研究提出一种基于元数据网络主题建模和朴素贝叶斯分类器的概率性情绪推荐系统，用于从Care Opinion平台分析患者体验，预测多标签情绪与二元情绪，显著优于基线模型，并提供R包和仪表板辅助个性化医疗。
摘要：
本研究提出了一种从在线患者体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来自Care Opinion的患者报告体验，揭示了与患者-照护者互动及临床结果相关的关键情感主题。我们开发了一个概率性、特定情境的情绪推荐系统，该系统使用朴素贝叶斯分类器，以具有情境意义的主题作为预测因子，能够预测多标签情绪和二元情绪。我们使用信息检索指标nDCG和Q-measure评估了该模型预测情绪相较于基线模型的优越性能，且我们预测的情绪达到了0.921的F1分数，显著优于标准情绪词典。该方法提供了一种透明且高性价比的方式来理解患者反馈，补充了传统收集方法并为个性化患者护理提供信息。我们的研究结果可通过R包和交互式仪表板访问，为医疗保健研究人员和从业者提供了有价值的工具。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
