Probabilistic emotion and sentiment modelling of patient-reported experiences
This provides a transparent, cost-effective tool for healthcare researchers and practitioners to analyze patient feedback, though it is incremental as it builds on existing topic modeling and classification methods.
The study tackled the problem of modeling patient emotions from online narratives by developing a probabilistic emotion and sentiment recommender system, achieving an F1 score of 0.921 for sentiment prediction and outperforming baseline models with metrics like nDCG and Q-measure.
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.