Abstract
Social networks play a vital role in public healthcare systems. Twitter, Facebook, and blogs have millions of health-related content and it is required to filter the data for the reduction of processing cost. A semi-supervised health classifier model is proposed for health care which analyzes the patient condition by symptoms and recommends either suggestions or treatments for the relevant diseases such as influenza, flu, etc. In a proposed system, ailments clusters are defined based on the features of diseases using Visualized Clustering Approach (VCA). The proposed Twitter classifier model effectively works for high-rated risk diseases when compared to the traditional healthcare model. Results are discussed in the experimental study.
This is a preview of subscription content, log in via an institution to check access.
References
Michael, J.P., Abeed, S., John, S.B., Azadeh, N., Matthew, S., Karen, L.S., Graciela, G.: Social media mining for public health monitoring and surveillance. In: Pacific Symposium on Biocomputing, pp. 468–479 (2016)
Batrinca, B., Philip, C.T.: Social media analytics: a survey of techniques, tools and platforms, AI & SOC 30, 89–116 (2014)
Culotta, A.: Towards detecting influenza epidemics by analyzing Twitter messages. In: KDD Workshop on Social Media Analytics (2010)
Culotta, A.: Lightweight methods to estimate influenza rates and alcohol sales volume from Twitter messages. In: Language Resources and Evaluation. Special Issue on Analysis of Short Texts on the Web (2012)
Lampos, V., Cristianini, N.: Tracking the flu pandemic by monitoring the social web. In: IAPR 2nd Workshop on Cognitive Information Processing (2012)
Maskawa, S., Aramaki, E., Morita, M.: Twitter catches the flu: detecting influenza epidemics using Twitter. In: Conference on Empirical Methods in Natural Language Processing (2010)
Lamb, A., Paul, M.J., Dredze, M.: Separating fact from fear: tracking flu infections on Twitter. In: Conference of the North American Chapter of the Association for Computational Linguistics (2013)
Chunara, R., Andrews, J., Brownstein, J.: Social and news media enable estimation of epidemiological patterns early in the 2010 Haitian cholera outbreak. Am. J. Trop. Med. Hyg. 86, 1 (2012)
Heaivilin, N., Gerbert, B., Page, J., Gibbs, J.: Public health surveillance of dental pain via Twitter. J. Dent. Res. 90, 9 (2011)
Bosley, J.C., Zhao, N.W., Hill, S., Shofer, F.S., Asch, D.A.: Decoding twitter: surveillance and trends for cardiac arrest and resuscitation communication. Resuscitation 84, 2 (2013)
Yoon, S., Elhadad, N., Bakken, S.: A practical approach for content mining of tweets. Am. J. Prev. Med. 45(1) (2013)
Golder, S., Macy, M.W.: Diurnal and seasonal mood varies with work, sleep and day length across diverse cultures. Science 333(6051), 1878–1881 (2011)
Moreno, M., Christakis, D.A., Egan, K.G., Brockman, L.N., Becker, T.: Associations between displayed alcohol references on Facebook and problem drinking among college students. Arch. Pediatr. Adolesc. Med. (2011)
Michael, J.P., Mark, D.: Discovering health topics in social media using topic models. Plos one 9(8), 1–14 (2014)
Blei, D.: Probabilistic topic models. Commun. ACM 55(4), 77–84 (2012)
Michael, J.P., Mark, D.: A model for mining public health topics from twitter, Johns Hopkins University (2011)
Blei, D., Ng, A., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. (2003)
Rajendra Prasad, K., Eswara Reddy, B.: An efficient visualized clustering approach for various datasets. In: IEEE SPICES, NIT Calicut (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Rajendra Prasad, K., Surya Prabha, I., Rajasekhar, N., Rajasekhar Reddy, M. (2018). Social Data Analytics by Visualized Clustering Approach for Health Care. In: Saeed, K., Chaki, N., Pati, B., Bakshi, S., Mohapatra, D. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 564. Springer, Singapore. https://doi.org/10.1007/978-981-10-6875-1_15
Download citation
DOI: https://doi.org/10.1007/978-981-10-6875-1_15
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-6874-4
Online ISBN: 978-981-10-6875-1
eBook Packages: EngineeringEngineering (R0)