A deep learning approach for predicting the quality of online health expert question-answering services

Autor: Haiqin Yang, Qing Chen, Z.L. Hu, Zhan Zhang, Decheng Zuo
Rok vydání: 2017
Předmět:
FOS: Computer and information sciences
Quality Control
Computer science
media_common.quotation_subject
Health Informatics
02 engineering and technology
computer.software_genre
Computer Science - Information Retrieval
Machine Learning
Deep belief network
020204 information systems
0202 electrical engineering
electronic engineering
information engineering
Feature (machine learning)
Question answering
Humans
Quality (business)
Set (psychology)
media_common
Computer Science - Computation and Language
Modalities
Consumer Health Information
business.industry
Deep learning
Semantics
Computer Science Applications
Multimodal learning
020201 artificial intelligence & image processing
Artificial intelligence
business
Computation and Language (cs.CL)
Delivery of Health Care
computer
Information Retrieval (cs.IR)
Natural language processing
Zdroj: Journal of Biomedical Informatics. 71:241-253
ISSN: 1532-0464
DOI: 10.1016/j.jbi.2017.06.012
Popis: Currently, a growing number of health consumers are asking health-related questions online, at any time and from anywhere, which effectively lowers the cost of health care. The most common approach is using online health expert question-answering (HQA) services, as health consumers are more willing to trust answers from professional physicians. However, these answers can be of varying quality depending on circumstance. In addition, as the available HQA services grow, how to predict the answer quality of HQA services via machine learning becomes increasingly important and challenging. In an HQA service, answers are normally short texts, which are severely affected by the data sparsity problem. Furthermore, HQA services lack community features such as best answer and user votes. Therefore, the wisdom of the crowd is not available to rate answer quality. To address these problems, in this paper, the prediction of HQA answer quality is defined as a classification task. First, based on the characteristics of HQA services and feedback from medical experts, a standard for HQA service answer quality evaluation is defined. Next, based on the characteristics of HQA services, several novel non-textual features are proposed, including surface linguistic features and social features. Finally, a deep belief network (DBN)-based HQA answer quality prediction framework is proposed to predict the quality of answers by learning the high-level hidden semantic representation from the physicians' answers. Our results prove that the proposed framework overcomes the problem of overly sparse textual features in short text answers and effectively identifies high-quality answers.
Comment: Submitted to Journal of Biomedical Informatics journal on Dec 10, 2016
Databáze: OpenAIRE
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