BMC Med Inform Decis Mak - Supporting meningitis diagnosis amongst infants and children through the use of fuzzy cognitive mapping.

Tópicos

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Resumo

CKGROUND: Meningitis is characterized by an inflammation of the meninges, or the membranes surrounding the brain and spinal cord. Early diagnosis and treatment is crucial for a positive outcome, yet identifying meningitis is a complex process involving an array of signs and symptoms and multiple causal factors which require novel solutions to support clinical decision-making. In this work, we explore the potential of fuzzy cognitive map to assist in the modeling of meningitis, as a support tool for physicians in the accurate diagnosis and treatment of the condition.METHODS: Fuzzy cognitive mapping (FCM) is a method for analysing and depicting human perception of a given system. FCM facilitates the development of a conceptual model which is not limited by exact values and measurements and thus is well suited to representing relatively unstructured knowledge and associations expressed in imprecise terms. A team of doctors (physicians), comprising four paediatricians, was formed to define the multifarious signs and symptoms associated with meningitis and to identify risk factors integral to its causality, as indicators used by clinicians to identify the presence or absence of meningitis in patients. The FCM model, consisting of 20 concept nodes, has been designed by the team of paediatricians in collaborative dialogue with the research team.RESULTS: The paediatricians were supplied with a form containing various input parameters to be completed at the time of diagnosing meningitis among infants and children. The paediatricians provided information on a total of 56 patient cases amongst children whose age ranged from 2 months to 7 years. The physicians' decision to diagnose meningitis was available for each individual case which was used as the outcome measure for evaluating the model. The FCM was trained using 40 cases with an accuracy of 95%, and later 16 test cases were used to analyze the accuracy and reliability of the model. The system produced the results with sensitivity of 83.3% and specificity of 80%.CONCLUSIONS: This work suggests that the application and development of a knowledge based system, using the formalization of FCMs for understanding the symptoms and causes of meningitis in children and infants, can provide a reliable front-end decision-making tool to better assist physicians.

Resumo Limpo

ckground mening character inflamm mening membran surround brain spinal cord earli diagnosi treatment crucial posit outcom yet identifi mening complex process involv array sign symptom multipl causal factor requir novel solut support clinic decisionmak work explor potenti fuzzi cognit map assist model mening support tool physician accur diagnosi treatment conditionmethod fuzzi cognit map fcm method analys depict human percept given system fcm facilit develop conceptu model limit exact valu measur thus well suit repres relat unstructur knowledg associ express imprecis term team doctor physician compris four paediatrician form defin multifari sign symptom associ mening identifi risk factor integr causal indic use clinician identifi presenc absenc mening patient fcm model consist concept node design team paediatrician collabor dialogu research teamresult paediatrician suppli form contain various input paramet complet time diagnos mening among infant children paediatrician provid inform total patient case amongst children whose age rang month year physician decis diagnos mening avail individu case use outcom measur evalu model fcm train use case accuraci later test case use analyz accuraci reliabl model system produc result sensit specif conclus work suggest applic develop knowledg base system use formal fcms understand symptom caus mening children infant can provid reliabl frontend decisionmak tool better assist physician

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