Novel approach for Data Mining of Social Media to Improve Health Care using Network-Based Modeling

Sonali More, Prof. P. P. Joshi


Social media provide a platform with tools to share information, to debate health care policy and practice issues, to promote health behaviors, to engage with the public, and to
educate and interact with patients. More recently the growth of online social networks and healthcare forums has led patients to voluntarily share information about their health, treatments, and drug use. In healthcare sector sentiment analysis gives relevant information to the consumer about a particular drug. We proposed framework to identify user communities (modules) and prospective users. For this work, we go through user opinion(positive or negative) as well as a side effect of drug present in that user comments. In the first stage, we use the LVQ algorithm to perform sentiment correlation between user posts positive or negative opinion on the drug. Further to identify influential user and to get consumer opinion we used network partition method based on optimizing stability quality measures. Our technique can make bigger studies into intelligently mining social media information for consumer opinion of diverse treatments and helps to get up-to-date and rapid feedback to treatment and also help healthcare industry to provide effective treatments of future remedies.

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A. Akay, A. Dragomir, and B. E. Erlandsson,Network-Based Modeling and Intelligent Data Mining of Social Media for Improving Care, IEEE journal of biomedical and health informatics, vol. 19, no. 1, january 2015.

A. Ochoa, A. Hernandez, L. Cruz, J. Ponce, F. Montes, L. Li, and

L. Janacek. Artificial societies and social simulation using ant colony,

particle swarm optimization and cultural algorithms, New Achievements in Evolutionary Computation, P. Korosec, Ed. Rijeka, Croatia: InTech, pp. 267297, 2010.

W. Cornell and W. Cornell. (2013). How Data Mining Drives Pharma:Information as a Raw Material and Product. [Online]. Available:

L. Dunbrack, Pharma 2.0 social media and pharmaceutical sales and marketing, in Proc. Health Ind. Insights, 2010, p. 7.

L. Getoor and C. Diehl, Link mining: a survey, SIGKDD Explor. Newsl., vol. 7, pp. 312, Dec. 2005.

Q. Lu. And and L. Getoor, Link-based classification, in Proc. 20th Int. Conf. Mach. Learning, Washington, D.C., USA, 2003, pp. 496503.

A. Ng, A. Zheng, and M. Jordan, Stable algorithms for link analysis, in Proc. SIGIR Conf. Inform. Retrieval., New Orleans, LouisianaLO, USA, 2001, pp. 258266.

K. Faust, Very local structure in social networks, Sociological Methodology, vol. 37,pp. 209256, Nov. 2007.

Huda Alhazmi, Swapna S. Gokhale, Derek Doran, Understanding Social Effects in Online Networks, Kno.e.sis Publications, 2015.

Kit Yan Chan*, C.K. Kwong and T.C. Wong, Modelling customer satisfaction for product development using genetic programming, Journal of Engineering Design Vol. 22, No. 1, January 2011, pp. 5568.

S. R. Das and M. Y. Chen, Yahoo! for Amazon: Sentiment extraction from small talk on the Web, Manag. Sci., vol. 53, pp. 13751388, Sep. 2007.

C. Corley, D. Cook, A. Mikler, and K. Singh, Text and structural data mining of influenza mentions in web and social media, Int. J. Environ. Res. Public Health, vol. 7, pp. 596615, Feb. 2010.

A. Akay, A. Dragomir, and B. E. Erlandsson, A novel data-mining approach leveraging social media to monitor consumer opinion of sitagliptin, J. Biomed Health Inform. Vol: PP, Issue: 99.

X. Feng, A. Cai, K. Dong, W. Chaing, M. Feng, N.S. Bhutada, J.

Inciardi, and T. Woldemariam, Assessing pancreatic cancer risk associated with dipeptidyl peptidase 4 inhibitors: data mining of FDA adverse event reporting system (FAERS),Pharmacovigilance, vol. 1, Jul. 2013.


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