VSE knjižnice (vzajemna bibliografsko-kataložna baza podatkov COBIB.SI)
  • Elicitation of neurological knowledge with argument-based machine learning
    Groznik, Vida ...
    Objective: The paper describes the use of expert's knowledge in practice and the efficiency of a recently developed technique called argument-based machinelearning (ABML) in the knowledge elicitation ... process. We are developinga 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 them reduce the number of patients in the "gray area" that require a very costly further examination (DaTSCAN). We strive to elicit comprehensible and medically meaningful knowledge in such a way that it does not come at the cost of diagnostic accuracy. Materials and methods: To alleviate the difficult problem of knowledge elicitation from data and domain experts, we used ABML. ABML guidesthe expert to explain critical special cases which cannot be handled automatically by machine learning. This very efficiently reduces the expert'sworkload, and combines expert's knowledge with learning data. 122 patients were enrolled into the study. Results: The classification accuracy ofthe final model was 91%. Equally important, the initial and the final modelswere also evaluated for their comprehensibility by the neurologists. All13 rules of the final model were deemed as appropriate to be able to support its decisions with good explanations. Conclusion: The paper demonstrates ABML's advantage in combining machine learning and expert knowledge. The accuracy of the system is very high with respect to the currentstate-of-the-art in clinical practice, and the system's knowledge base is assessed to be very consistent from a medical point of view. This opens upthe possibility to use the system also as a teaching tool.
    Vir: Artificial intelligence in medicine. - ISSN 0933-3657 (Vol. 57, no. 2, spec. iss., 2013, str. 133-144)
    Vrsta gradiva - članek, sestavni del
    Leto - 2013
    Jezik - angleški
    COBISS.SI-ID - 30199257

    Povezava(-e):

    http://www.sciencedirect.com/science/article/pii/S093336571200111X
    DOI

vir: Artificial intelligence in medicine. - ISSN 0933-3657 (Vol. 57, no. 2, spec. iss., 2013, str. 133-144)
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