ICTACT Journal on Soft Computing

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An Approach for Auto-Generating Solution to User-Generated Medical Content Using Deep Learning Techniques


Affiliations
1 Department of Computer Engineering, Vishwakarma Institute of Information Technology, India
     

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One of many things humans are obsessive about is health. Presently, when faced with a health-related issue one goes to the web first, to find closure to his/her problem. The community Question Answering (cQA) forum allows people to pose their query and/or discuss it. Due to alike or unique nature of the health query it may go unanswered. Many a time the answers provided are ill-founded, leaving the user discontent. This indicates that the process is dependent on supplementary users or experts, in relation to their ability and/or the time taken to answer the question. Hence, the need to create an answer predictor which provides instant and better-quality result. We, therefore propose a novel scheme where deep learning is used to produce appropriate answer to the given health query. Both historical data i.e. cQA and general medical data are used to form a powerful Knowledge Base (KB), to assist the health predictor.

Keywords

Community Question Answering, Deep Learning, Health- Related Issue.
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Abstract Views: 258

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  • An Approach for Auto-Generating Solution to User-Generated Medical Content Using Deep Learning Techniques

Abstract Views: 258  |  PDF Views: 2

Authors

Faraz Bagwan
Department of Computer Engineering, Vishwakarma Institute of Information Technology, India
Leena Deshpande
Department of Computer Engineering, Vishwakarma Institute of Information Technology, India

Abstract


One of many things humans are obsessive about is health. Presently, when faced with a health-related issue one goes to the web first, to find closure to his/her problem. The community Question Answering (cQA) forum allows people to pose their query and/or discuss it. Due to alike or unique nature of the health query it may go unanswered. Many a time the answers provided are ill-founded, leaving the user discontent. This indicates that the process is dependent on supplementary users or experts, in relation to their ability and/or the time taken to answer the question. Hence, the need to create an answer predictor which provides instant and better-quality result. We, therefore propose a novel scheme where deep learning is used to produce appropriate answer to the given health query. Both historical data i.e. cQA and general medical data are used to form a powerful Knowledge Base (KB), to assist the health predictor.

Keywords


Community Question Answering, Deep Learning, Health- Related Issue.

References