HealthQA: A Chinese QA Summary System for Smart Health

Yin, Yanshen; Zhang, Yong; Liu, Xiao; Zhang, Yan; Xing, Chunxiao; Chen, Hsinchun

doi:10.1007/978-3-319-08416-9_6

Cited by 16 publications

(7 citation statements)

References 16 publications

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“…Answer summarization was firstly proposed by Liu et al [7]. Yin et al developed a hierarchical clustering method to group similar questions and a ranking based summarization for representing an answer [8].…”

Section: Related Workmentioning

confidence: 99%

An answer summarization method based on keyword extraction

Fan

Fang

2017

BIO Web Conf.

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Abstract. In order to reduce the redundancy of answer summary generated from community q&a dataset without topic tags, we propose an answer summarization algorithm based on keyword extraction. We combine tf-idf with word vector to change the influence transferred ratio equation in TextRank. And then during summarizing, we take the ratio of the number of sentences containing any keyword to the total number of candidate sentences as an adaptive factor for AMMR. Meanwhile we reuse the scores of keywords generated by TextRank as a weight factor for sentence similarity computing. Experimental results show that the proposed answer summarization is better than the traditional MMR and AMMR.

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Section: Related Workmentioning

confidence: 99%

An answer summarization method based on keyword extraction

Fan

Fang

2017

BIO Web Conf.

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“…These crowdsourced health care information websites are widely accepted by patients [ 19 ]. This service is a type of expert-based crowdsourced medical service [ 20 , 21 ], which allows patients to post an “open-call” question to undefined doctors [ 22 ] with relatively low cost [ 16 , 23 ]. Crowdsourced health care information websites have adopted an active crowdsourcing mode, that is, the patient has an active role, wherein he/she poses a particular medical question and solicits relevant information, knowledge, opinion, and ideas from doctors [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

Factors Influencing Doctors’ Participation in the Provision of Medical Services Through Crowdsourced Health Care Information Websites: Elaboration-Likelihood Perspective Study

Si¹,

Wu²,

Qing³

2020

JMIR Med Inform

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Background Web-based crowdsourcing promotes the goals achieved effectively by gaining solutions from public groups via the internet, and it has gained extensive attention in both business and academia. As a new mode of sourcing, crowdsourcing has been proven to improve efficiency, quality, and diversity of tasks. However, little attention has been given to crowdsourcing in the health sector. Objective Crowdsourced health care information websites enable patients to post their questions in the question pool, which is accessible to all doctors, and the patients wait for doctors to respond to their questions. Since the sustainable development of crowdsourced health care information websites depends on the participation of the doctors, we aimed to investigate the factors influencing doctors’ participation in providing health care information in these websites from the perspective of the elaboration-likelihood model. Methods We collected 1524 questions with complete patient-doctor interaction processes from an online health community in China to test all the hypotheses. We divided the doctors into 2 groups based on the sequence of the answers: (1) doctor who answered the patient’s question first and (2) the doctors who answered that question after the doctor who answered first. All analyses were conducted using the ordinary least squares method. Results First, the ability of the doctor who first answered the health-related question was found to positively influence the participation of the following doctors who answered after the first doctor responded to the question (βoffline1=.177, P<.001; βoffline2=.063, P=.048; βonline=.418, P<.001). Second, the reward that the patient offered for the best answer showed a positive effect on doctors’ participation (β=.019, P<.001). Third, the question’s complexity was found to positively moderate the relationships between the ability of the first doctor who answered and the participation of the following doctors (β=.186, P=.05) and to mitigate the effect between the reward and the participation of the following doctors (β=–.003, P=.10). Conclusions This study has both theoretical and practical contributions. Online health community managers can build effective incentive mechanisms to encourage highly competent doctors to participate in the provision of medical services in crowdsourced health care information websites and they can increase the reward incentives for each question to increase the participation of the doctors.

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“…Globally, aging problems are becoming increasingly prominent. When aging is accompanied by higher rates of chronic diseases, such as hypertension, diabetes, cancer and cardiac disease, the result is severe shortages of medical resources such as physicians, nurses, hospital rooms and health educators [1].…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Yang

et al. 2017

Journal of Biomedical Informatics

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Recently, online health expert question-answering (HQA) services (systems) have attracted more and more health consumers to ask health-related questions everywhere at any time due to the convenience and effectiveness. However, the quality of answers in existing HQA systems varies in different situations. It is significant to provide effective tools to automatically determine the quality of the answers. Two main characteristics in HQA systems raise the difficulties of classification: (1) physicians' answers in an HQA system are usually written in short text, which yields the data sparsity issue; (2) HQA systems apply the quality control mechanism, which refrains the wisdom of crowd. The important information, such as the best answer and the number of users' votes, is missing. To tackle these issues, we prepare the first HQA research data set labeled by three medical experts in 90days and formulate the problem of predicting the quality of answers in the system as a classification task. We not only incorporate the standard textual feature of answers, but also introduce a set of unique non-textual features, i.e., the popular used surface linguistic features and the novel social features, from other modalities. A multimodal deep belief network (DBN)-based learning framework is then proposed to learn the high-level hidden semantic representations of answers from both textual features and non-textual features while the learned joint representation is fed into popular classifiers to determine the quality of answers. Finally, we conduct extensive experiments to demonstrate the effectiveness of including the non-textual features and the proposed multimodal deep learning framework.

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HealthQA: A Chinese QA Summary System for Smart Health

Cited by 16 publications

References 16 publications

An answer summarization method based on keyword extraction

An answer summarization method based on keyword extraction

Factors Influencing Doctors’ Participation in the Provision of Medical Services Through Crowdsourced Health Care Information Websites: Elaboration-Likelihood Perspective Study

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

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