J Med Syst - Knowledge extraction algorithm for variances handling of CP using integrated hybrid genetic double multi-group cooperative PSO and DPSO.

Tópicos

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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Although the clinical pathway (CP) predefines predictable standardized care process for a particular diagnosis or procedure, many variances may still unavoidably occur. Some key index parameters have strong relationship with variances handling measures of CP. In real world, these problems are highly nonlinear in nature so that it's hard to develop a comprehensive mathematic model. In this paper, a rule extraction approach based on combing hybrid genetic double multi-group cooperative particle swarm optimization algorithm (PSO) and discrete PSO algorithm (named HGDMCPSO/DPSO) is developed to discovery the previously unknown and potentially complicated nonlinear relationship between key parameters and variances handling measures of CP. Then these extracted rules can provide abnormal variances handling warning for medical professionals. Three numerical experiments on Iris of UCI data sets, Wisconsin breast cancer data sets and CP variances data sets of osteosarcoma preoperative chemotherapy are used to validate the proposed method. When compared with the previous researches, the proposed rule extraction algorithm can obtain the high prediction accuracy, less computing time, more stability and easily comprehended by users, thus it is an effective knowledge extraction tool for CP variances handling.

Resumo Limpo

although clinic pathway cp predefin predict standard care process particular diagnosi procedur mani varianc may still unavoid occur key index paramet strong relationship varianc handl measur cp real world problem high nonlinear natur hard develop comprehens mathemat model paper rule extract approach base comb hybrid genet doubl multigroup cooper particl swarm optim algorithm pso discret pso algorithm name hgdmcpsodpso develop discoveri previous unknown potenti complic nonlinear relationship key paramet varianc handl measur cp extract rule can provid abnorm varianc handl warn medic profession three numer experi iri uci data set wisconsin breast cancer data set cp varianc data set osteosarcoma preoper chemotherapi use valid propos method compar previous research propos rule extract algorithm can obtain high predict accuraci less comput time stabil easili comprehend user thus effect knowledg extract tool cp varianc handl

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