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Social Health Records: Gaining Insights into Public Health Behaviors, Emotions, and Disease Trajectories

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Public Health Intelligence and the Internet

Part of the book series: Lecture Notes in Social Networks ((LNSN))

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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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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Authors and Affiliations

  1. City University of New York, New York, NY, USA

    Soon Ae Chun

  2. New Jersey Institute of Technology, Newark, NJ, USA

    James Geller

  3. The Bloomberg L.P., New York, NY, USA

    Xiang Ji

Authors
  1. Soon Ae Chun
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  2. James Geller
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  3. Xiang Ji
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Corresponding author

Correspondence to Soon Ae Chun .

Editor information

Editors and Affiliations

  1. Department of Pediatrics, Center for Biomedical Informatics, The University of Tennessee Health Science Center—Oak-Ridge National Laboratory (UTHSC-ORNL), Memphis, Tennessee, USA

    Arash Shaban-Nejad

  2. Department of Pediatrics, Harvard Medical School, Boston Children’s Hospital, Harvard University, Boston, Massachusetts, USA

    John S. Brownstein

  3. Department of Epidemiology and Biostatistics and Occupational Health, McGill Clinical & Health Informatics, McGill University, Montreal, Québec, Canada

    David L. Buckeridge

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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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  • DOI: https://doi.org/10.1007/978-3-319-68604-2_2

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