Skip to main content
SpringerLink
Log in
Book cover

Machine Learning in Healthcare Informatics pp 131–145Cite as

Rule Learning in Healthcare and Health Services Research

  • Chapter
  • First Online:

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 56))

Abstract

Successful application of machine learning in healthcare requires accuracy, transparency, acceptability, ability to deal with complex data, ability to deal with background knowledge, efficiency, and exportability. Rule learning is known to satisfy the above criteria. This chapter introduces rule learning in healthcare, presents very expressive attributional rules, briefly describes the AQ21 rule learning system, and discusses three application areas in healthcare and health services research.

This is a preview of subscription content, 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
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

Learn about institutional subscriptions

References

  1. Bloedorn E, Wnek J, Michalski RS, Kaufman K (1993) AQ17 A multistrategy learning system the method and users guide. reports of the machine learning and inference laboratory: MLI 93-12. School of Information Technology and Engineering. George Mason University, Fairfax

    Google Scholar 

  2. Bloedorn E (1996) Multistrategy constructive induction. Ph.D. Dissertation, Reports of the machine learning and inference laboratory : MLI 96-7. School of Information Technology and Engineering. George Mason University, Fairfax

    Google Scholar 

  3. Bloedorn E, Michalski RS (1998) Data-driven constructive induction. IEEE intelligent systems special issue on feature transformation and subset selection: 30–37

    Google Scholar 

  4. Hripcsak G (1994) Writing arden syntax medical logic modules. Comput Biol Med 5(24):331–363

    Article  Google Scholar 

  5. Michalski RS (1969) On the quasi-minimal solution of the general covering problem. In: Proceedings of the 5th international symposium on information processing (FCIP 69) (Switching Circuits). A3: 125–128. Yugoslavia, Bled

    Google Scholar 

  6. Michalski RS, Larson J (1975) AQVAL/1 (AQ7) User’s guide and program description. Department of Computer Science. University of Illinois, Urbana

    Google Scholar 

  7. Michalski RS, Larson J (1983) Incremental generation of VL1 hypotheses: the underlying methodology and the description of program AQ11. Department of computer science: reports of the intelligent systems group, ISG 83-5, UIUCDCS-F-83-905. University of Illinois, Urbana

    Google Scholar 

  8. Michalski RS, Kaufman K (2001) The AQ19 system for machine learning and pattern discovery: a general description and user’s guide. Reports of the machine learning and inference laboratory: MLI 01-2. George Mason University, Fairfax

    Google Scholar 

  9. Michalski RS (2004) Attributional calculus: a logic and representation language for natural induction. Reports of the machine learning and inference laboratory: MLI 04-2 George Mason University, Fairfax

    Google Scholar 

  10. Wnek J, Michalski RS (1994) Hypothesis-driven constructive induction in AQ17-HCI: a method and experiments. Mach Learn 14(2):139–168

    Article  MATH  Google Scholar 

  11. Wnek J, Kaufman K, Bloedorn E, Michalski RS (1996) Inductive learning system AQ15c: The method and user’s guide. Reports of the machine learning and inference laboratory: MLI 96-6 George Mason University, Fairfax

    Google Scholar 

  12. Wojtusiak J (2004) AQ21 User’s guide. Reports of the machine learning and inference laboratory: MLI 04-3 George Mason University, Fairfax

    Google Scholar 

  13. Wojtusiak J, Michalski RS, Kaufman K, Pietrzykowski J (2006) The AQ21 natural induction program for pattern discovery: initial version and its novel features. In: Proceedings of the 18th IEEE international conference on tools with artificial intelligence. Washington, D.C

    Google Scholar 

  14. Wojtusiak J, Michalski RS, Simanivanh T, Baranova AV (2009) Towards application of rule learning to the meta-analysis of clinical data: an example of the metabolic syndrome. Int J Med Informatics 78(12):104–111

    Article  Google Scholar 

  15. Wojtusiak J, Alemi F (2010) Analyzing decisions using datasets with multiple attributes: a machine learning approach. In: Yuehwern Y (ed) Handbook of healthcare delivery systems. CRC Press, Boca Raton

    Google Scholar 

  16. Wojtusiak J, Shiver J, Ngufor C, Ewald R (2011) Machine learning in hospital billing management. presentation. In: HIMSS 2011 acedemic forum. AUPHA, Orlando

    Google Scholar 

  17. Zurada JM, Mazurowski MA, Abdullin A, Ragade R, Wojtusiak J, Gentle JE (2009) Building virtual community in computational intelligence and machine learning. Comput Intell Mag 4(1):43–54

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Machine Learning and Inference Laboratory, Department of Health Administration and Policy, George Mason University, Fairfax, VA, USA

    Janusz Wojtusiak

Authors
  1. Janusz Wojtusiak
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Janusz Wojtusiak .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Louisiana Tech University, Ruston, Louisiana, USA

    Sumeet Dua

  2. Ngee Ann Polytechnic, Singapore, Singapore

    U. Rajendra Acharya

  3. Department of Health Informatics and Information Management, Louisiana Tech University, Ruston, Louisiana, USA

    Prerna Dua

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Wojtusiak, J. (2014). Rule Learning in Healthcare and Health Services Research. In: Dua, S., Acharya, U., Dua, P. (eds) Machine Learning in Healthcare Informatics. Intelligent Systems Reference Library, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40017-9_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-40017-9_7

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40016-2

  • Online ISBN: 978-3-642-40017-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation