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Identifying Service Gaps from Public Patient Opinions Through Text Mining

  • Conference paper
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Intelligent Computing and Internet of Things (ICSEE 2018, IMIOT 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 924))

Abstract

Nowadays, healthcare systems have become increasingly patient-centered and the unstructured, open-ended and patient-driven feedback has drawn a significant attention from medical and healthcare organizations. Based on this, we are motivated to harness various machine learning algorithms to process such a large amount of unstructured comments posted on public patient opinion sites. We first used sentiment analysis to automatically predict the concerns of patients from the training set which was already labelled. Then, with the help of the clustering, we extracted the hot topics related to a specific domain to reflect the service issues that patients concern most. Through experimental studies, the performance of different algorithms and the influence of different parameter were compared. Finally, refering to the survey and previous studies, the results were analyzed to obtain the conclusions.

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Author information

Authors and Affiliations

  1. School of Management, Chongqing Technology and Business University, Chongqing, 400067, China

    Min Tang, Yiping Liu & Zhiguo Li

  2. School of Engineering, Cardiff University, Cardiff, CF24 3AA, UK

    Ying Liu

Authors
  1. Min Tang
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  2. Yiping Liu
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  3. Zhiguo Li
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  4. Ying Liu
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Corresponding author

Correspondence to Yiping Liu .

Editor information

Editors and Affiliations

  1. The University of Leeds, Leeds, UK

    Kang Li

  2. Shanghai University, Shanghai, China

    Minrui Fei

  3. Shanghai University, Shanghai, China

    Dajun Du

  4. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Zhile Yang

  5. Northeastern University, Shenyang, China

    Dongsheng Yang

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Tang, M., Liu, Y., Li, Z., Liu, Y. (2018). Identifying Service Gaps from Public Patient Opinions Through Text Mining. In: Li, K., Fei, M., Du, D., Yang, Z., Yang, D. (eds) Intelligent Computing and Internet of Things. ICSEE IMIOT 2018 2018. Communications in Computer and Information Science, vol 924. Springer, Singapore. https://doi.org/10.1007/978-981-13-2384-3_10

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  • DOI: https://doi.org/10.1007/978-981-13-2384-3_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2383-6

  • Online ISBN: 978-981-13-2384-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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