• Corpus ID: 15581809

licitation of neurological knowledge with argument-based machine learning

@inproceedings{Groznika2013licitationON,
  title={licitation of neurological knowledge with argument-based machine learning},
  author={ida Groznika and M. G. Guida and A. Sadikova and Martin Mo{\vz}inaa and Dejan Georgievb and eronika Kragelj and Samo Ribaric and Zvezdan Pirto{\vs}ekb and Ivan Bratkoa},
  year={2013}
}
Objective: The paper describes the use of expert’s knowledge in practice and the efficiency of a recently developed technique called argument-based machine learning (ABML) in the knowledge elicitation process. We are developing a neurological decision support system to help the neurologists differentiate between three types of tremors: Parkinsonian, essential, and mixed tremor (comorbidity). The system is intended to act as a second opinion for the neurologists, and most importantly to help… 

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References

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Elicitation of Neurological Knowledge with ABML

TLDR
A decision support system to help the neurologists differentiate between three types of tremors: Parkinsonian, essential, and mixed tremor (co-morbidity) is developed.

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TLDR
This paper shows how argument based machine learning (ABML) is accomplished through a case study of building a knowledge base of an expert system used in a chess tutoring application.

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Argument based machine learning

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This research has received support from the following agencies: Defense Advanced Research Projects Agency, DAHC 15-73-C-0435; National Institutes of Health, 5R24-RR00612, RR-00785; National Science Foundation, MCS 76-11649, DCR 74-23461; The Bureau of Health Sciences Research and Evaluation, HS -01544.

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