Abstract
Social media and personal health monitoring devices (e.g., Fitbit) provide abundant patient-generated health-related data. These open health data, generated via patient engagement and sharing, are referred to as Social Health Records (SHR) as opposed to the EHR (Electronic Health Records) that are created and entered by clinicians. SHRs are changing the healthcare paradigm from the authoritative provider-centric model to a collaborative and patient-oriented healthcare framework. This chapter proposes an SHR Integration and Analytics Framework to leverage Social Health Records for gaining insights into population-level and individual-level healthcare practices and behaviors, as well as emotions. The framework defines a pipeline for generating knowledge from the social health data sources to the end users, including the patients themselves, public health officials, and healthcare providers. The SHR integration and analytics framework build a coherent knowledge base, linking the Social Health Records that are “spilled” in distributed online social media, with other online health information sources, such as results from authoritative medical research. The semantic integration model of heterogeneous health data sources provides population-level health analytics and reasoning capabilities to gain intelligence on public healthcare issues and practices. The SHR is shown to be a valuable resource for epidemic surveillance systems with real-time monitoring. We focus on an approach to quantifying the SHR-based public emotions for measuring health concern levels and for tracking them, and propose SHR-based predictive models to infer individual-level and population-level comorbidity predictions and comorbidity progression trajectories.
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References
Raghupathi, W., Raghupathi, V.: Big data analytics in healthcare: promise and potential. Health Inf. Sci. Syst. 2, 1–10 (2014)
Househ, M., Borycki, E., Kushniruk, A.: Empowering patients through social media: the benefits and challenges. Health Inf. J. 20, 50–58 (2014)
Ji X, Chun, S.A., Geller, J.: Monitoring public health concerns using Twitter sentiment classifications. In: Proceedings of IEEE International Conference on Healthcare Informatics, pp. 335–344. Philadelphia, PA (2013)
Smith, C.A., Wicks, P.J.: PatientsLikeMe: consumer health vocabulary as a folksonomy. In: Proceedings of American Medical Informatics Association Annual Symposium, pp. 682–686. Washington D.C. (2008)
Bizer, C.: Evolving the web into a global data space. In: Fernandes, A.A., Gray, A.G., Belhajjame, K. (eds.) Proceedings of 28th British National Conference on Databases, p. 1. Springer, Manchester, UK (2011)
Bizer, C., Heath, T., Berners-Lee, T.: Linked data—the story so far. Int. J. Semant. Web Inf. Syst. 5, 1–22 (2009)
Harth, A., Gil, Y.: Geospatial data integration with linked data and provenance tracking. In: W3C/OGC Linking Geospatial Data Workshop, pp. 1–5 (2014)
Specia, L., Motta, E.: Integrating folksonomies with the semantic web. In: Proceedings of the 4th European Conference on The Semantic Web: Research and Applications, pp. 624–639. Springer, Innsbruck, Austria (2007)
Fox, P., McGuinness, D.L., Cinquini, L., et al.: Ontology-supported scientific data frameworks: the virtual solar-terrestrial observatory experience. Comput. Geosci. 35, 724–738 (2009)
Chun, S.A., MacKellar, B.: Social health data integration using semantic Web. In: Proceedings of the 27th Annual ACM Symposium on Applied Computing, pp. 392–397 (2012)
MacKellar, B., Schweikert, C., Chun, S.A.: Patient-centered clinical trials decision support using linked open data. Int. J. Softw. Sci. Comput. Intell. 6, 31–48 (2014)
Tofferi, J.K., Jackson, J.L., O’Malley, P.G.: Treatment of fibromyalgia with cyclobenzaprine: a meta-analysis. Arthritis Rheum. 51, 9–13 (2004)
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retrieval 2(1–2), 1–135 (2008)
Zhuang, L., Jing, F., Zhu, X.-Y.: Movie review mining and summarization. In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management, pp. 43–50. Arlington, VAS (2006)
Chew, C., Eysenbach, G.: Pandemics in the age of Twitter: content analysis of Tweets during the 2009 H1N1 outbreak. PLoS ONE 5(11), e14118 (2010)
Chawla, N.V., Davis, D.A.: Bringing big data to personalized healthcare: a patient-centered framework. J. Gen. Intern. Med. 28, 660–665 (2013)
Davis, D.A., Chawla, N.V., Christakis, N.A., Barabasi, A.L.: Time to CARE: a collaborative engine for practical disease prediction. Data Min. Knowl. Disc. 20, 388–415 (2010)
S. Hassan and Z. Syed, “From netflix to heart attacks: collaborative filtering in medical datasets,” in Proceedings of the 1st ACM International Health Informatics Symposium, Arlington, Virginia, USA, 2010, pp. 128–134
Folino, F., Pizzuti, C.: A comorbidity-based recommendation engine for disease prediction. In: Proceedings of the IEEE 23rd International Symposium on Computer-Based Medical Systems, pp. 6–12. Bentley, Australia (2010)
Qian, B., Wang, X., Cao, N., Li, H., Jiang, Y.-G.: A relative similarity based method for interactive patient risk prediction. Data Min. Knowl. Disc. 29, 1070–1093 (2015)
Hussein, A.S., Omar, W.M., Li, X., Hatem, M.A.: Smart collaboration framework for managing chronic disease using recommender system. Health Syst. 3, 12–17 (2014)
Jensen, A.B., Moseley, P.L., Oprea, T.I., Ellesøe, S.G., Eriksson, R., Schmock, H., et al.: Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients. Nat. Commun. 5 (2014)
Wang, X., Sontag, D., Wang, F.: Unsupervised learning of disease progression models. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 85–94. New York, NY (2014)
Hainke, K., Rahnenführer, J., Fried, R.: Disease progression models: a review and comparison. Dortmund University, Technical Report (2011)
Ji, X., Chun, S.A., Geller, J., Oria, V.: Collaborative and trajectory prediction models of medical conditions by mining patients’ social data. In: Proceedings of 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 695–700. Washington D.C. (2015)
Ji, X., Chun, S., Geller, J.: Predicting comorbid conditions and trajectories using social health records. IEEE Trans. Nanobiosci. 15(4):371–379 (2016)
Ji, X., Chun, S.A., Geller, J.: Epidemic outbreak and spread detection system based on twitter data. In: Proceedings of the First International Conference on Health Information Science, pp. 152–163. Beijing, China (2012)
PHP Simple HTML DOM Parser. http://simplehtmldom.sourceforge.net. Accessed 14 Apr 2014
CDC Prevalence Data of Asthma in 2010. http://www.cdc.gov/asthma/brfss/2010/brfssdata.htm. Accessed 14 Apr 2014
Behavioral Risk Factor Surveillance System. http://www.cdc.gov/brfss/. Accessed 14 Apr 2014
Rao, D., McNamee, P., Dredze, M.: Entity linking: finding extracted entities in a knowledge base. In: Poibeau, T., Saggion, H., Piskorski, J., Yangarber, R. (eds.) Multi-source, Multilingual Information Extraction and Summarization. Theory and Applications of Natural Language Processing, pp. 93–115. Springer, Berlin (2013)
Hogan, A., Zimmermann, A., Umbrich, J., Polleres, A., Decker, S.: Scalable and distributed methods for entity matching, consolidation and disambiguation over linked data corpora. Web Semant. 10, 76–110 (2012). doi:10.1016/j.websem.2011.11.002
Hassanzadeh, O., Kementsietsidis, A., Lim, L., Miller, R.J., Wang, M.: LinkedCT: a linked data space for clinical trials. CoRR abs/0908.0567 (2009)
Chun, S.A., MacKellar, B.: Social health data integration using semantic Web. In: Proceedings of the 27th Annual ACM Symposium on Applied Computing, pp. 392–397. Trento, Italy (2012)
Bodenreider, O.: The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucleic Acids Res. 32(Database issue), D267–270 (2004). doi:10.1093/nar/gkh061
Ji, X., Chun, S.A., Geller, J.: Social InfoButtons: integrating open health data with social data using semantic technology. In: Proceedings of the Fifth Workshop on Semantic Web Information Management, New York (2013)
SPARQL Query Language for RDF. http://www.w3.org/TR/rdf-sparql-query/. Accessed 14 Apr 2014
Collins, S.A., Currie, L.M., Bakken, S., Cimino, J.J.: Information needs, Infobutton Manager use, and satisfaction by clinician type: a case study. (1067–5027 (Print)) (2009)
Cimino, J.J., Elhanan, G., Zeng, Q.: Supporting infobuttons with terminological knowledge. In: Proceedings of AMIA Annual Fall Symposium, pp. 528–532. AMIA, Bethesda, MD (1997)
Cimino, J.J.: Use, usability, usefulness, and impact of an infobutton manager. In: Proceedings of American Medical Informatics Association Annual Symposium, pp. 151–155. AMIA, Bethesda, MD (2006)
Cimino, J.J., Li, J., Allen, M., Currie, L.M., Graham, M., Janetzki, V., Lee, N.J., Bakken, S., Patel, V.L.: Practical considerations for exploiting the World Wide Web to create infobuttons. Medinfo 11, 277–281 (2004)
Ji, X., Chun, S.A., Wei, Z., Geller, J.: Twitter sentiment classification for measuring public health concerns. Soc. Netw. Anal. Min. 5, 1–25 (2015)
Ji, X., Chun, S., Geller, J.: Knowledge-based tweet classification for disease sentiment monitoring. In: Pedrycz, W., Chen S.-M. (eds.) Sentiment Analysis and Ontology Engineering: An Environment of Computational Intelligence, pp. 425–454. Springer (2016)
Acknowledgements
The research work reported in this paper was partially funded by PSC-CUNY Research Foundation under the award numbers #64266 and #65232. The main research was carried out as part of the dissertation work by X. Ji at NJIT. The dataset was collected in the year 2012 when it is freely available. The data was processed right after.
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Chun, S.A., Geller, J., Ji, X. (2017). Social Health Records: Gaining Insights into Public Health Behaviors, Emotions, and Disease Trajectories. In: Shaban-Nejad, A., Brownstein, J., Buckeridge, D. (eds) Public Health Intelligence and the Internet. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-68604-2_2
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