A Decision Making System Based on Complementary Learning

  • Tuan Zea Tan
  • Geok See Ng
  • Chai Quek
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 4)

Abstract

Medical decision making is often linked to survival and wellbeing of patients. As such, it is paramount for clinical decision support system to not only provides human-understandable explanation, but also human-relatable reasoning. A human decision making model Complementary Decision Making System (CDMS) is proposed. CDMS is based on complementary learning that functionally models the lateral inhibition and segregation mechanisms observed in the human decision making in prefrontal and parietal lobes. As such, CDMS has a human-like process of decision making. A mapping of hypothetico-deductive reasoning further equip CDMS with a decision making flow that is akin to physicians. CDMS was subsequently applied in diagnosis and it demonstrated its potential as a clinical decision support system.

Keywords

Decision Making Receptive Field Fuzzy Rule Lateral Inhibition Reward System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Tuan Zea Tan
    • 1
  • Geok See Ng
    • 2
  • Chai Quek
    • 3
  1. 1.Cancer Science Institute of SingaporeNational University of Singapore, Centre for Life ScienceSingapore
  2. 2.School of Engineering (Electronics)Nanyang PolytechnicSingapore
  3. 3.Centre for Computational IntelligenceNanyang Technological UniversitySingapore

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