Int J Med Inform - Artificial intelligence techniques applied to the development of a decision-support system for diagnosing celiac disease.

Tópicos

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Resumo

CKGROUND: Celiac disease (CD) is a difficult-to-diagnose condition because of its multiple clinical presentations and symptoms shared with other diseases. Gold-standard diagnostic confirmation of suspected CD is achieved by biopsying the small intestine.OBJECTIVE: To develop a clinical decision-support system (CDSS) integrated with an automated classifier to recognize CD cases, by selecting from experimental models developed using intelligence artificial techniques.METHODS: A web-based system was designed for constructing a retrospective database that included 178 clinical cases for training. Tests were run on 270 automated classifiers available in Weka 3.6.1 using five artificial intelligence techniques, namely decision trees, Bayesian inference, k-nearest neighbor algorithm, support vector machines and artificial neural networks. The parameters evaluated were accuracy, sensitivity, specificity and area under the ROC curve (AUC). AUC was used as a criterion for selecting the CDSS algorithm. A testing database was constructed including 38 clinical CD cases for CDSS evaluation. The diagnoses suggested by CDSS were compared with those made by physicians during patient consultations.RESULTS: The most accurate method during the training phase was the averaged one-dependence estimator (AODE) algorithm (a Bayesian classifier), which showed accuracy 80.0%, sensitivity 0.78, specificity 0.80 and AUC 0.84. This classifier was integrated into the web-based decision-support system. The gold-standard validation of CDSS achieved accuracy of 84.2% and k=0.68 (p<0.0001) with good agreement. The same accuracy was achieved in the comparison between the physician's diagnostic impression and the gold standard k=0. 64 (p<0.0001). There was moderate agreement between the physician's diagnostic impression and CDSS k=0.46 (p=0.0008).CONCLUSIONS: The study results suggest that CDSS could be used to help in diagnosing CD, since the algorithm tested achieved excellent accuracy in differentiating possible positive from negative CD diagnoses. This study may contribute towards developing of a computer-assisted environment to support CD diagnosis.

Resumo Limpo

ckground celiac diseas cd difficulttodiagnos condit multipl clinic present symptom share diseas goldstandard diagnost confirm suspect cd achiev biopsi small intestineobject develop clinic decisionsupport system cdss integr autom classifi recogn cd case select experiment model develop use intellig artifici techniquesmethod webbas system design construct retrospect databas includ clinic case train test run autom classifi avail weka use five artifici intellig techniqu name decis tree bayesian infer knearest neighbor algorithm support vector machin artifici neural network paramet evalu accuraci sensit specif area roc curv auc auc use criterion select cdss algorithm test databas construct includ clinic cd case cdss evalu diagnos suggest cdss compar made physician patient consultationsresult accur method train phase averag onedepend estim aod algorithm bayesian classifi show accuraci sensit specif auc classifi integr webbas decisionsupport system goldstandard valid cdss achiev accuraci k p good agreement accuraci achiev comparison physician diagnost impress gold standard k p moder agreement physician diagnost impress cdss k pconclus studi result suggest cdss use help diagnos cd sinc algorithm test achiev excel accuraci differenti possibl posit negat cd diagnos studi may contribut toward develop computerassist environ support cd diagnosi

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