Skip to main content
SpringerLink
Log in

Exploring Regularity in Traditional Chinese Medicine Clinical Data Using Heterogeneous Weighted Networks Embedding

  • Conference paper
  • First Online:
Database Systems for Advanced Applications (DASFAA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11448))

Included in the following conference series:

Abstract

Regularities of prescriptions are important for both clinical practice and novel healthcare development in clinical traditional Chinese medicine (TCM). To address this issue and meet clinical demand for determining treatments, we propose an unsupervised analysis model termed AMNE to determine effective herbs for diverse symptoms in prescriptions. Results confirmed by human physicians demonstrate AMNE can outperform several previous TCM regularity discovery models in prescriptions.

The work is supported by National Natural Science Foundation of China (No. 61672161), Youth Research Fund of Shanghai municipal health and Family Planning Commission (No. 2015Y0195).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Chen, Y., Wang, C.: HINE: heterogeneous information network embedding. In: DASFAA, pp. 180–195 (2017)

    Google Scholar 

  2. Huang, E.W., Wang, S., Liu, B., Liu, B., Zhou, X., Zhai, C.X.: PaReCat: patient record subcategorization for precision traditional Chinese medicine. In: ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, pp. 443–452 (2016)

    Google Scholar 

  3. Ruan, C., et al.: THCluster: herb supplements categorization for precision traditional Chinese medicine. In: BIBM, pp. 417–424 (2017)

    Google Scholar 

  4. Wang, S., et al.: A conditional probabilistic model for joint analysis of symptoms, diseases, and herbs in traditional Chinese medicine patient records. In: BIBM, pp. 411–418 (2017)

    Google Scholar 

  5. Yao, L., Zhang, Y., Wei, B., Zhang, W., Jin, Z.: A topic modeling approach for traditional Chinese medicine prescriptions. IEEE Trans. Knowl. Data Eng. 1(99), PP (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science, Fudan University, Shanghai, China

    Chunyang Ruan & Yanchun Zhang

  2. College of Engineering and Science, Victoria University, Melbourne, Australia

    Ye Wang & Yanchun Zhang

  3. Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China

    Chunyang Ruan & Yanchun Zhang

  4. Department of Oncology and Longhua Hospital, Shanghai, China

    Yun Yang

Authors
  1. Chunyang Ruan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ye Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yanchun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yun Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yanchun Zhang .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Guoliang Li

  2. Duke University, Durham, NC, USA

    Jun Yang

  3. University of Porto, Porto, Portugal

    Joao Gama

  4. Chiang Mai University, Chiang Mai, Thailand

    Juggapong Natwichai

  5. Beihang University, Beijing, China

    Yongxin Tong

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ruan, C., Wang, Y., Zhang, Y., Yang, Y. (2019). Exploring Regularity in Traditional Chinese Medicine Clinical Data Using Heterogeneous Weighted Networks Embedding. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11448. Springer, Cham. https://doi.org/10.1007/978-3-030-18590-9_35

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-18590-9_35

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-18589-3

  • Online ISBN: 978-3-030-18590-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation