MC

Current Trends in Science and Technology

an Open Access Publication ISSN: 0976-9730 | 0976-9498

Computer Science

Diagnosis of Various Diseases Using Big Data Extraction From Question Answering Website

Mr.Fulsundar Ajinkya A., Mr. Walunj Amol S.,Mr. Bhalerao Akash P. Prof. Jadhav N.S.
1Dept. of Computer Engineering Jaihind Collage Of Engineering, Kuran Pune, India ajinkyafulsundar@gmail.com 2Dept. of Computer Engineering Jaihind Collage Of Engineering, Kuran Pune, India walunjamol444@gmail.com 3Dept. of Computer Engineering Jaihind Collage oF Engineering, Kuran Pune, India akashbhalerao201@gmail.com 4Dept. of Computer Engineering Jaihind Collage Of Engineering, Kuran Pune, India nilteshjadhav5547@gmail.com
Online First: February 21, 2018
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Abstract

The medical crowd sourced question answering (Q&A) websites are booming in recent years, and increasingly large amount of patients and doctors are involved. The valuable information from these medical crowd sourced Q&A websites can benefit patients, Doctors and the society. One key to unleash the power of these Q&A websites is to extract medical knowledge from the noisy question-answer pairs and filter out unrelated or even incorrect information. Facing the daunting scale of information generated on medical Q&A websites every day, It is unrealistic to fulfill this task via supervised method due to the expensive annotation cost. In this system, We propose a Medical Knowledge Extraction (MKE) system that can automatically provide high quality knowledge triples extracted from the noisy question-answer pairs, and at the same time, estimate expertise for the doctors who give answers on these Q&A websites. The MKE system is built upon a truth discovery framework, where we jointly estimate trustworthiness of answers and doctor expertise from the data without any supervision. We further tackle three unique challenges in the medical knowledge extraction task, namely representation of noisy input, multiple linked truths, and the long-tail phenomenon in the data. The MKE system is applied on real-world datasets crawled from xywy.com, one of the most popular medical crowd sourced Q&A websites. Both quantitative evaluation and case studies demonstrate that the proposed MKE system can successfully provide useful medical knowledge and accurate doctor expertise. We further demonstrate a real-world application, Ask A Doctor, which can automatically give patients suggestions to their questions.

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Feb 21, 2018
Published
Feb 21, 2018
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References

[1]. X. Yin, J. Han, and P. S. Yu, “Truth discovery with multiple conflicting information providers on the web,” in SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’07), 2007 [2]. X. L. Dong, L. Berti-Equille, and D. Srivastava, “Integrating conflicting data: The role of source dependence,” The Proceedings of the VLDB Endowment (PVLDB),2009. [3]. J. Pasternack and D. Roth, “Knowing what to believe (when you already know something),” in Proc. of the International Conference on Computational Linguistics (COLING’10), 2010. [4]. L. Nie, M. Akbari, T. Li, and T.-S. Chua, “A joint local-global approach for medical terminology assignment,” in SIGIR 2014 [5]. L. Nie, Y.-L. Zhao, M. Akbari, J. Shen, and T.-S. Chua, “Bridging the vocabulary gap between health seekers and healthcare knowledge,” IEEE Transactions on Knowledge and Data Engineering,2015. [6]. Yaliang Li, Chaochun Liu, Jing Gao, Qi Li, Nan Du,Wei Fan Extracting Medical Knowledge from Crowdsourced Question Answering Website 2016 IEEE [7]. Liqiang Nie, Yi-Liang Zhao, Mohammad Akbari, Jialie Shen, Tat-Seng Chua “Bridging the Vocabulary Gap between Health Seekers and Healthcare Knowledge” IEEE. [8]. Jeff Pasternack, Dan Roth “Knowing What to Believe (When you already know something) ” issue 6- (ICRTEST) January 2017 p-ISSN: 2394-8280. [9]. Xin Luna Dong, Laure BertiEquille, Divesh Srivastava “Integrating Conflicting Data: The Role of Source Dependence ” 2009 IEEE. [10]. Xiaoxin Yin, Jiawei Han, Philip S. Yu “Truth Discovery with Multiple Conflicting Information Providers on the Web” IEEE transactions on systems, man, and cybernetics: systems, vol. 6, no. 4, pp. 2321–3361, April 2007.
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References

[1]. X. Yin, J. Han, and P. S. Yu, “Truth discovery with multiple conflicting information providers on the web,” in SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’07), 2007
[2]. X. L. Dong, L. Berti-Equille, and D. Srivastava, “Integrating conflicting data: The role of source dependence,” The Proceedings of the VLDB Endowment (PVLDB),2009.
[3]. J. Pasternack and D. Roth, “Knowing what to believe (when you already know something),” in Proc. of the International Conference on Computational Linguistics (COLING’10), 2010.
[4]. L. Nie, M. Akbari, T. Li, and T.-S. Chua, “A joint local-global approach for medical terminology assignment,” in SIGIR 2014
[5]. L. Nie, Y.-L. Zhao, M. Akbari, J. Shen, and T.-S. Chua, “Bridging the vocabulary gap between health seekers and healthcare knowledge,” IEEE Transactions on Knowledge and Data Engineering,2015.
[6]. Yaliang Li, Chaochun Liu, Jing Gao, Qi Li, Nan Du,Wei Fan Extracting Medical Knowledge from Crowdsourced Question Answering Website 2016 IEEE
[7]. Liqiang Nie, Yi-Liang Zhao, Mohammad Akbari, Jialie Shen, Tat-Seng Chua “Bridging the Vocabulary Gap between Health Seekers and Healthcare Knowledge” IEEE.
[8]. Jeff Pasternack, Dan Roth “Knowing What to Believe (When you already know something) ” issue 6- (ICRTEST) January 2017 p-ISSN: 2394-8280.
[9]. Xin Luna Dong, Laure BertiEquille, Divesh Srivastava “Integrating Conflicting Data: The Role of Source Dependence ” 2009 IEEE.
[10]. Xiaoxin Yin, Jiawei Han, Philip S. Yu “Truth Discovery with Multiple Conflicting Information Providers on the Web” IEEE transactions on systems, man, and cybernetics: systems, vol. 6, no. 4, pp. 2321–3361, April 2007.
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