Understanding what patients think about hospitals: A deep learning approach for detecting emotions in patient opinions

Abstract

Introduction: Most hospital assessment systems are based on the study of objective statistical variables as well as patient opinions on their experiences with respect to the services offered by each hospital. Nevertheless, studies have indicated that most of these assessment systems fail to detect patient emotions when they are assessing their stays in a hospital. This information is vital to understanding most of the patient reviews, which are very complex and convey several emotions per review. Therefore, this study aimed to address the problem of detecting multiple emotions from patient reviews.

Methods: First, a large set of patient opinions was collected from a website that allowed patients to publish their experiences when visiting hospitals. Second, each opinion was labeled with the corresponding conveyed emotions. Third, a deep learning architecture based on a bidirectional gated recurrent unit with a multichannel convolutional neural network layer was proposed to detect multiple emotions from these reviews. Finally, the hyperparameters of this architecture were fine-tuned and different pretrained word embedding models were configured to test its performance.

Results: The results confirmed that our proposed method outperformed other deep learning and machine learning-based algorithms and achieved an average accuracy of 95.82%. Furthermore, the experiments show that clinical-domain word embedding slightly outperforms other general-domain word embeddings, although general-domain embeddings are larger in terms of dimensions.

Conclusions: The combination of the gated recurrent unit and the multichannel convolutional neural network is able to exploit both semantic and syntactic characteristics of patient opinions. The findings of this study identify research gaps related to areas such as opinion-based hospital recommendations, thereby providing future research directions.

Keywords: Bidirectional gated recurrent unit; Convolutional neural network; Multichannel; Multiple emotion classification; Patient feedback.