Elicitation of Neurological Knowledge with ABML

  • Vida Groznik
  • Matej Guid
  • Aleksander Sadikov
  • Martin Možina
  • Dejan Georgiev
  • Veronika Kragelj
  • Samo Ribarič
  • Zvezdan Pirtošek
  • Ivan Bratko
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6747)

Abstract

The paper describes the process of knowledge elicitation for a neurological decision support system. To alleviate the difficult problem of knowledge elicitation from data and domain experts, we used a recently developed technique called ABML (Argument Based Machine Learning). The paper demonstrates ABML’s advantage in combining machine learning and expert knowledge. ABML guides the expert to explain critical special cases which cannot be handled automatically by machine learning. This very efficiently reduces the expert’s workload, and combines it with automatically learned knowledge. We developed a decision support system to help the neurologists differentiate between three types of tremors: Parkinsonian, essential, and mixed tremor (co-morbidity). The system is intended to act as a second opinion for the neurologists, and most importantly to help them reduce the number of patients in the “gray area” that require a very costly further examination (DaTSCAN).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Vida Groznik
    • 1
  • Matej Guid
    • 1
  • Aleksander Sadikov
    • 1
  • Martin Možina
    • 1
  • Dejan Georgiev
    • 2
  • Veronika Kragelj
    • 3
  • Samo Ribarič
    • 3
  • Zvezdan Pirtošek
    • 2
  • Ivan Bratko
    • 1
  1. 1.Artificial Intelligence Laboratory, Faculty of Computer and Information ScienceUniversity of LjubljanaSlovenia
  2. 2.Department of NeurologyUniversity Medical Centre LjubljanaSlovenia
  3. 3.Faculty of MedicineUniversity of LjubljanaSlovenia

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