Skip to main content
SpringerLink
Log in

Mining for Actionable Knowledge in Tinnitus Datasets

  • Chapter
  • First Online:
Thriving Rough Sets

Part of the book series: Studies in Computational Intelligence ((SCI,volume 708))

Abstract

This chapter describes the application of decision and action rules mining to the problem area of tinnitus treatment and characterization. The chapter presents the process of a Tinnitus Retraining Therapy treatment protocol, which is to be automatized with classification and action rules. The tinnitus dataset collected at Emory University School of Medicine in Atlanta, as well as preprocessing steps performed on the data are described. Next, a series of experiments on association and action rule extraction are presented. Selected outcome rules are listed in a form of medical hypotheses. An analysis and interpretation of sample rules are provided together with their validation in accordance with expert medical knowledge.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover 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. Jastreboff, P.J., Jastreboff, M.M.: Tinnitus retraining therapy (TRT) as a method for treatment of tinnitus and hyperacusis patients. J.-Am. Acad. Audiol. 11(3), 162–177 (2000)

    Google Scholar 

  2. Ras, Z.W., Wieczorkowska, A.: Action-rules: How to increase profit of a company. In: Principles of Data Mining and Knowledge Discovery, pp. 587–592. Springer (2000)

    Google Scholar 

  3. Wasyluk, H., Ras, Z.W., Wyrzykowska, E.: Application of action rules to HEPAR clinical decision support system. Experimental and Clinical Hepatology 4, 46–48 (2008)

    Google Scholar 

  4. Ras, Z.W., Wieczorkowska, A.: Advances in Music Information Retrieval. Studies in Computational Intelligence, vol. 274. Springer (2010)

    Google Scholar 

  5. Pawlak, Z., Marek, W.: Rough sets and information systems. ICS. PAS. Reports 441, 481–485 (1981)

    Google Scholar 

  6. Im, S., Ras, Z., Tsay, L.-S.: Action reducts. In: Foundations of Intelligent Systems, pp. 62–69. Springer (2011)

    Google Scholar 

  7. Touati, H., Ras, Z.W., Studnicki, J., Wieczorkowska, A.: Mining surgical Meta-actions effects with variable diagnoses’ number. In: Foundations of Intelligent Systems, pp. 254–263. Springer (2014)

    Google Scholar 

  8. Wang, J.: Encyclopedia of Business Analytics and Optimization. IGI Global (2014)

    Google Scholar 

  9. Simunek, M.: LISp-Miner Control Language description of scripting language implementation. J. Syst. Integration 5(2), 28–44 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of North Carolina, Charlotte, USA

    Katarzyna A. Tarnowska & Zbigniew W. Ras

  2. Warsaw University of Technology, Polish-Japanese Academy of IT, Warsaw, Poland

    Zbigniew W. Ras

  3. Emory University School of Medicine, Atlanta, USA

    Pawel J. Jastreboff

Authors
  1. Katarzyna A. Tarnowska
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zbigniew W. Ras
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pawel J. Jastreboff
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Katarzyna A. Tarnowska .

Editor information

Editors and Affiliations

  1. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China

    Guoyin Wang

  2. Institute of Mathematics, University of Warsaw Institute of Mathematics, Warsaw, Poland

    Andrzej Skowron

  3. University of Regina, Department of Computer Science University of Regina, Regina, Saskatchewan, Canada

    Yiyu Yao

  4. Institute of Informatics, University of Warsaw, Warsaw, Poland

    Dominik Ślęzak

  5. Polish-Japanese Academy IT , Warsaw, Poland

    Lech Polkowski

Appendix: Attribute Definition

Appendix: Attribute Definition

Table 4 The definition of attributes related to tinnitus patients and their visits in LISp-Miner: an attribute’s group, name (short), meaning, type, number of categories, sample value(s)
Full size table

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Cite this chapter

Tarnowska, K.A., Ras, Z.W., Jastreboff, P.J. (2017). Mining for Actionable Knowledge in Tinnitus Datasets. In: Wang, G., Skowron, A., Yao, Y., Ślęzak, D., Polkowski, L. (eds) Thriving Rough Sets. Studies in Computational Intelligence, vol 708. Springer, Cham. https://doi.org/10.1007/978-3-319-54966-8_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-54966-8_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54965-1

  • Online ISBN: 978-3-319-54966-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation