Toward sustainable virtualized healthcare: Extracting medical entities from chinese online health consultations using deep neural networks

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.