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Clustering Analysis on Patient-Physician Communication and Shared Decision-Making During Cancer Prognosis Discussion

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Abstract

We conducted clustering analyses to verify whether common patterns can be identified from two distinct types of end-stage cancer prognosis discussion information, including patient-centered communication and shared decision making data. We applied hierarchical clustering on patient-centered communication measurement data and frequent itemset hierarchical clustering on coded shared decision-making interaction information. Our results suggested modest association between the two data sets in clustering assignments when measuring it with a normalized mutual information index. However, we could not find any noticeable overlap between the two assignments.

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Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Tsinghua University, Beijing, China

    Yikang Li

  2. School of Industrial Engineering, Purdue University, West Lafayette, IN, USA

    Yuxuan Liu

  3. Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA

    Nan Kong

  4. Department of Human Development and Family Studies, Purdue University, West Lafayette, IN, USA

    Cleveland G. Shields

Authors
  1. Yikang Li
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  2. Yuxuan Liu
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  3. Nan Kong
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  4. Cleveland G. Shields
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Corresponding author

Correspondence to Nan Kong .

Editor information

Editors and Affiliations

  1. Institute of Automation,Bldg.1004, Chinese Academy of Sciences, Beijing, China

    Xiaolong Zheng

  2. University of Arizona, Tucson, Arizona, USA

    Daniel Dajun Zeng

  3. University of Arizona, Phoenix, USA

    Hsinchun Chen

  4. Mayo Clinic, Scottsdale, USA

    Scott J. Leischow

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Li, Y., Liu, Y., Kong, N., Shields, C.G. (2016). Clustering Analysis on Patient-Physician Communication and Shared Decision-Making During Cancer Prognosis Discussion. In: Zheng, X., Zeng, D., Chen, H., Leischow, S. (eds) Smart Health. ICSH 2015. Lecture Notes in Computer Science(), vol 9545. Springer, Cham. https://doi.org/10.1007/978-3-319-29175-8_8

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  • DOI: https://doi.org/10.1007/978-3-319-29175-8_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-29174-1

  • Online ISBN: 978-3-319-29175-8

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