In this paper, we propose an attention-based multi-task neural network model for text classification and sequence tagging and then apply it to the named entity recognition and the intent analysis of Chinese online medical questions. We found that the...
Disability and rehabilitation. Assistive technology
Jun 18, 2020
People with disabilities or special needs can benefit from AI-based conversational agents (i.e., chatbots) that are used for competence training and well-being management. Assessing the quality of interactions with these chatbots is key to being abl...
BMC medical informatics and decision making
May 27, 2020
BACKGROUND: Patient experience surveys often include free-text responses. Analysis of these responses is time-consuming and often underutilized. This study examined whether Natural Language Processing (NLP) techniques could provide a data-driven, hos...
Text classification (TC) is the task of automatically assigning documents to a fixed number of categories. TC is an important component in many text applications. Many of these applications perform preprocessing. There are different types of text pre...
Clinical Named Entity Recognition (CNER) is a critical task which aims to identify and classify clinical terms in electronic medical records. In recent years, deep neural networks have achieved significant success in CNER. However, these methods requ...
Named entity recognition is a fundamental and crucial task in medical natural language processing problems. In medical fields, Chinese clinical named entity recognition identifies boundaries and types of medical entities from unstructured text such a...
This study conducts a mapping study to survey the landscape of health chatbots along three research questions: What illnesses are chatbots tackling? What patient competences are chatbots aimed at? Which chatbot technical enablers are of most interest...
BMC medical informatics and decision making
Apr 4, 2019
BACKGROUND: Twitter messages (tweets) contain various types of topics in our daily life, which include health-related topics. Analysis of health-related tweets would help us understand health conditions and concerns encountered in our daily lives. In...
According to NHTSA, more than 3477 people (including 551 non-occupants) were killed and 391,000 were injured due to distraction-related crashes in 2015. The distracted driving epidemic has long been under research to identify its impact on driving be...
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