BACKGROUND: 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.
BACKGROUND: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.
Authors: Kristen Miller; Danielle Mosby; Muge Capan; Rebecca Kowalski; Raj Ratwani; Yaman Noaiseh; Rachel Kraft; Sanford Schwartz; William S Weintraub; Ryan Arnold Journal: J Am Med Inform Assoc Date: 2018-05-01 Impact factor: 4.497
Authors: Jonas F Ludvigsson; Jyotishman Pathak; Sean Murphy; Matthew Durski; Phillip S Kirsch; Christophe G Chute; Euijung Ryu; Joseph A Murray Journal: J Am Med Inform Assoc Date: 2013-08-16 Impact factor: 4.497
Authors: Greta Falavigna; Giorgio Costantino; Raffaello Furlan; James V Quinn; Andrea Ungar; Roberto Ippoliti Journal: Intern Emerg Med Date: 2018-10-23 Impact factor: 3.397