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A Collaborative Framework Based for Semantic Patients-Behavior Analysis and Highlight Topics Discovery of Alcoholic Beverages in Online Healthcare Forums

  • Transactional Processing Systems
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Abstract

Medical data in online groups and social media contain valuable information, which is provided by both healthcare professionals and patients. In fact, patients can talk freely and share their personal experiences. These resources are a valuable opportunity for health professionals who can access patients’ opinions, as well as discussions between patients. Recently, the data processing of the health community and, how to extract knowledge is a significant technical challenge. There are many online group and forums that users can discuss on healthcare issues. Therefore, we can examine these text documents for discovering knowledge and evaluating patients’ behavior based on their opinions and discussions. For example, there are many questions and answering groups on Twitter or Facebook. Given the importance of the research, in this paper, we present a semantic framework based on topic model (LDA) and Random forest(RF) to predict and retrieval latent topics of healthcare text-documents from an online forum. We extract our healthcare records (patient-questions) from patient.info website as a real dataset. Experiments on our dataset show that social media forums could help for detecting significant patient safety problems on healthcare issues.

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Acknowledgements

This article has been awarded by the National Natural Science Foundation of China (61941113, 81674099, 61502233), the Fundamental Research Fund for the Central Universities (30918015103, 30918012204), Nanjing Science and Technology Development Plan Project (201805036), and “13th Five-Year” equipment field fund (61403120501), China Academy of Engineering Consulting Research Project(2019-ZD-1-02-02), National Social Science Foundation (18BTQ073).

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Authors and Affiliations

  1. School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, 210094, China

    Hamed Jelodar, Yongli Wang, Mahdi Rabbani & Ruxin Zhao

  2. Science and Technology on Complex Systems Simulation Laboratory, Beijing, 100101, China

    Gang Xiao

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  1. Hamed Jelodar
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  2. Yongli Wang
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  3. Mahdi Rabbani
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  4. Gang Xiao
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  5. Ruxin Zhao
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Correspondence to Hamed Jelodar or Yongli Wang.

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Hamed Jelodar, Yongli Wang, Mahdi Rabbani,Gang Xiao, Ruxin Zho declare no conflict of interest directly related to the submitted work.

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Jelodar, H., Wang, Y., Rabbani, M. et al. A Collaborative Framework Based for Semantic Patients-Behavior Analysis and Highlight Topics Discovery of Alcoholic Beverages in Online Healthcare Forums. J Med Syst 44, 101 (2020). https://doi.org/10.1007/s10916-020-01547-0

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