Artif Intell Med - Elicitation of neurological knowledge with argument-based machine learning.

Tópicos

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Resumo

JECTIVE: 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 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 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 expert's knowledge with learning data. 122 patients were enrolled into the study.RESULTS: The classification accuracy of the final model was 91%. Equally important, the initial and the final models were also evaluated for their comprehensibility by the neurologists. All 13 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 current state-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 up the possibility to use the system also as a teaching tool.

Resumo Limpo

jectiv paper describ use expert knowledg practic effici recent develop techniqu call argumentbas machin learn abml knowledg elicit process develop neurolog decis support system help neurologist differenti three type tremor parkinsonian essenti mix tremor comorbid system intend act second opinion neurologist import help reduc number patient gray area requir cost examin datscan strive elicit comprehens medic meaning knowledg way come cost diagnost accuracymateri method allevi difficult problem knowledg elicit data domain expert use abml abml guid expert explain critic special case handl automat machin learn effici reduc expert workload combin expert knowledg learn data patient enrol studyresult classif accuraci final model equal import initi final model also evalu comprehens neurologist rule final model deem appropri abl support decis good explanationsconclus paper demonstr abml advantag combin machin learn expert knowledg accuraci system high respect current stateoftheart clinic practic system knowledg base assess consist medic point view open possibl use system also teach tool

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