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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  • X.Y. Peng, Y. Chen and Z.W. Huang, “A Chinese Question Answering System using Web Service on Restricted Domain”, Proceedings of International Conference on Artificial Intelligence and Computational Intelligence, pp. 350-353, 2010.

  • H. Zhang, L. Zhu, S. Xu and W. Li, “Xml-Based Document Retrieval in Chinese Diseases Question Answering System”, Mobile, Ubiquitous, and Intelligent Computing, Vol. 274, pp. 211-217. 2014.

  • Kishore Papineni, Salim Roukos, Todd Ward and Wei-Jing Zhu, “BLEU: A Method for Automatic Evaluation of Machine Translation”, Proceedings of 40th Annual Meeting of the Association for Computational Linguistics, pp. 311318, 2002.

  • Kazuma Hashimoto et al., “A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks”, Proceedings of International Conference on Empirical Methods in Natural Language Processing, pp. 1-7, 2017.

  • Trevor Hastie, Robert Tibshirani and Jerome Friedman, “The Elements of Statistical Learning: Data Mining, Inference, and Prediction”, Springer, 2001.

  • Liqiang Nie, Xiaochi Wei, Dongxiang Zhang, Xiang Wang, Zhipeng Gao and Yi Yang. “Data-Driven Answer Selection in Community QA Systems”, IEEE Transactions on Knowledge and Data Engineering, Vol. 29, No. 6, pp. 413-421, 2017.

  • Zongcheng Ji, Zhengdong Lu and Hang Li, “An Information Retrieval Approach to Short Text Conversation”, Proceedings of International Conference on Computation and Language, pp. 23-27, 2014.

  • Lifeng Shang, Zhengdong Lu and Hang Li, “Neural Responding Machine for Short-Text Conversation”, Proceedings of 53rd Annual Meeting of the Association for Computational Linguistics, pp. 1577-1586, 2015.

  • D.A. Davis, N.V. Chawla, N. Blumm, N. Christakis and A.L. Barabasi, “Predicting Individual Disease Risk based on Medical History”, Proceedings of International Conference on Information and Knowledge Management, pp. 773-778, 2008.

  • S. Doan and H. Xu, “Recognizing Medication Related Entities in Hospital Discharge Summaries using Support Vector Machine”, Proceedings of International Conference on Computational Linguistics, pp. 330-337, 2010.

  • I. Batal, L. Sacchi, R. Bellazzi and M. Hauskrecht, “A Temporal Abstraction Framework for Classifying Clinical Temporal Data”, Proceedings of American Medical Informatics Association, pp. 227-234, 2008.

  • K. Latha and R. Rajaram, “Improvisation of Seeker Satisfaction in Yahoo! Community Question Answering Portal”, ICTACT Journal on Soft Computing, Vol. 1, No. 3 pp. 152-162, 2011.

  • R. Fakoor, F. Ladhak, A. Nazi and M. Huber, “Using Deep Learning to Enhance Cancer Diagnosis and Classification”, Proceedings of International Conference on Machine Learning, pp. 291-297, 2013.

  • M. Shouman, T. Turner and R. Stocker, “Using Decision Tree for Diagnosing Heart Disease Patients”, Proceedings of 9th Australasian Data Mining Conference, pp. 336-343, 2011.

  • Y. Zhang and B. Liu, “Semantic Text Classification of Disease Reporting”, Proceedings of the International ACM SIGIR Conference, pp. 43-49, 2007.

  • J. Zhou, L. Yuan, J. Liu and J. Ye, “A Multi-Task Learning Formulation for Predicting Disease Progression”, Proceedings of ACM International Conference on Knowledge Discovery and Data Mining, pp. 881-885, 2011.

  • B. Koopman, P. Bruza, L. Sitbon and M. Lawley, “Evaluating Medical Information Retrieval”, Proceedings of International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 123-128, 2011.

  • P. Sondhi, J. Sun, H. Tong and C. Zhai, “Sympgraph: A Framework for Mining Clinical Notes through Symptom Relation Graphs”, Proceedings of International ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 21-24, 2012.

  • S. Ghumbre, C. Patil and A. Ghatol, “Heart Disease Diagnosis using Support Vector Machine”, Proceedings of International Conference on Computer Science and Information Technology, pp. 12-16, 2011.

  • Sebastian Basterrech, Jan Janousek and Vaclav Snasel, “A Performance Study of Random Neural Network as Supervised Learning Tool using CUDA”, Journal of Internet Technology, Vol. 17, No. 4, pp. 771-778, 2016.

  • S.H. Yang, S.P. Crain and H. Zha, “Bridging the Language Gap: Topic Adaptation for Documents with Different Technicality”, Proceedings of 14th International Conference on Artificial Intelligence and Statistics, pp. 91-95, 2011.

  • Alan Ritter, Colin Cherry and William B. Dolan, “DataDriven Response Generation in Social Media”, Proceedings of Conference on Empirical Methods in Natural Language Processing, pp. 141-146, 2011.

  • F. Wang, N. Lee, J. Hu, J. Sun, S. Ebadollahi and A. Laine, “A Framework for Mining Signatures from Event Sequences and its Applications in Healthcare Data”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, No. 2, pp. 272-285, 2013.

  • F. Wang, N. Lee, J. Hu, J. Sun and S. Ebadollahi, “Towards Heterogeneous Temporal Clinical Event Pattern Discovery: A Convolutional Approach”, Proceedings of International Conference on Knowledge Discovery and Data Mining, pp.178-183, 2012.

  • A. Khosla, Y. Cao, C. C.Y. Lin, H.K. Chiu, J. Hu and H. Lee,” An Integrated Machine Learning Approach to Stroke Prediction”, Proceedings of International Conference on Knowledge Discovery and Data Mining, pp. 30-34, 2010.

  • K. Karpagam and A. Saradha, “An Intelligent Conversation Agent for Health Care Domain”, ICTACT Journal on Soft Computing, Vol. 4, No. 3, pp. 772-776, 2014.


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

Abstract Views: 317  |  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