Increasingly popular virtualized healthcare services such as online health consultations have significantly changed the way in which health information is sought, and can alleviate geographic barriers, time constraints, and medical resource shortage problems. These online patient–doctor communications have been generating abundant amounts of healthcare-related data. Medical entity extraction from these data is the foundation of medical knowledge discovery, including disease surveillance and adverse drug reaction detection, which can potentially enhance the sustainability of healthcare. Previous studies that focus on health-related entity extraction have certain limitations such as demanding tough handcrafted feature engineering, failing to extract out-of-vocabulary entities, and being unsuitable for the Chinese social media context. Motivated by these observations, this study proposes a novel model named CNMER (Chinese Medical Entity Recognition) using deep neural networks for medical entity recognition in Chinese online health consultations. The designed model utilizes Bidirectional Long Short-Term Memory and Conditional Random Fields as the basic architecture, and uses character embedding and context word embedding to automatically learn effective features to recognize and classify medical-related entities. Exploiting the consultation text collected from a prevalent online health community in China, the evaluation results indicate that the proposed method significantly outperforms the related state-of-the-art models that focus on the Chinese medical entity recognition task. We expect that our model can contribute to the sustainable development of the virtualized healthcare industry.

Purpose: The purpose of this study is to identify medical service quality factors that patients care about and establish a medical service quality evaluation index system by analyzing online reviews of medical and healthcare service platforms in combination with a questionnaire survey.Design/methodology/approach: This study adopts a combination of review mining and questionnaire surveys. The latent Dirichlet allocation (LDA) model was used to mine hospital reviews on the medical and healthcare service platform to obtain the medical service quality factors that patients pay attention to, and then the questionnaire was administered to obtain the relative importance of these factors to patients' perception of service quality. Finally, the index system was established.Findings: The medical service quality factors patients care about include medical skills and ethics, registration service, operation effect, consulting communication, drug therapy, diagnosis process and medical equipment.Research limitations/implications: The identification of medical service quality factors provides a reference for medical institutions to improve their medical service quality.Originality/value: This study uses online review mining to obtain medical service quality factors from the perspective of patients, which is different from previous methods of obtaining factors from relevant literature or expert judgments; then, based on the mining results, a medical service quality evaluation index system is established by using questionnaire data.

Purpose: Recommending suitable content for users of online health communities (OHCs) is critical for overcoming information overload problem and facilitate medical decision making, but remains not fully investigated. This study aims to provide a content recommendation approach to automatically match valuable health-related information for OHC members.Design/methodology/approach: A framework of health-related content recommendation was proposed by leveraging rich social information in online communities. The authors constructed user influence relationship (UIR) utilizing users' interaction records, user profiles and user-generated content. The initial user rating matrix and the user post matching matrix were then created by analyzing text content of posts. Finally, the user rating matrix and the recommended content were generated for community members. Datasets were collected from an OHC to evaluate the effectiveness of the proposed approach.Findings: The experimental results revealed that the proposed method statistically outperformed baseline models in content recommendation for users of OHCs.Research limitations/implications: The incorporation of social information can significantly enhance the performance of content recommendation in OHCs. The user post matching degree based on text analysis can improve the effectiveness of recommendation.Practical implications: This study potentially contributes to the social support exchange and medical decision making of community members and the sustainable prosperity of OHCs.Originality/value: This study proposes a novel social content recommendation method for online health consumers based on UIRs by leveraging social information in OHCs. The results indicate the significance of social information in content recommendation of healthcare social media.

Purpose: Online health communities (OHCs) are platforms that help health consumers to communicate with each other and obtain social support for better healthcare outcomes. However, it is usually difficult for community members to efficiently find appropriate peers for social support exchange due to the tremendous volume of users and their generated content. Most of the existing user recommendation systems fail to effectively utilize the rich social information in social media, which can lead to unsatisfactory recommendation performance. The purpose of this study is to propose a novel user recommendation method for OHCs to fill this research gap.Design/methodology/approach: This study proposed a user recommendation method that utilized the adapted matrix factorization (MF) model. The implicit user behavior networks and the user influence relationship (UIR) network were constructed using the various social information found in OHCs, including user-generated content (UGC), user profiles and user interaction records. An experiment was conducted to evaluate the effectiveness of the proposed approach based on a dataset collected from a famous online health community.Findings: The experimental results demonstrated that the proposed method outperformed all baseline models in user recommendation using the collected dataset. The incorporation of social information from OHCs can significantly improve the performance of the proposed recommender system.Practical implications: This study can help users build valuable social connections efficiently, enhance communication among community members, and potentially contribute to the sustainable prosperity of OHCs.Originality/value: This study introduces the construction of the UIR network in OHCs by integrating various social information. The conventional MF model is adapted by integrating the constructed UIR network for user recommendation.

In order to improve the efficiency of fault diagnosis, a novel granular computing algorithm is developed to reduce computational cost. It is realized by extracting and partitioning on the complete graphs, and in the process of graph generation, the dimensionless similarity method is proposed to overcome the influence of attributes with different dimensions. So, the algorithm is named graph partition based on dimensionless similarity (GPDS). Moreover, similarity threshold determination method based on frequency distribution histogram is proposed to reduce the dependency on the experiences of experts. Finally, different characteristic data are applied to verify the theories. The experimental results indicate that the compressed training samples can maintain the classification accuracy. Furthermore, the elapsed time can be obviously reduced. Therefore, the GPDS method can be used in fault diagnosis properly.