AIM: To investigate the role of artificial neural networks in predicting the presence of thyroid disease in atrophic body gastritis patients. METHODS: A dataset of 29 input variables of 253 atrophic body gastritis patients was applied to artificial neural networks (ANNs) using a data optimisation procedure (standard ANNs, T&T-IS protocol, TWIST protocol). The target variable was the presence of thyroid disease. RESULTS: Standard ANNs obtained a mean accuracy of 64.4% with a sensitivity of 69% and a specificity of 59.8% in recognizing atrophic body gastritis patients with thyroid disease. The optimization procedures (T&T-IS and TWIST protocol) improved the performance of the recognition task yielding a mean accuracy, sensitivity and specificity of 74.7% and 75.8%, 78.8% and 81.8%, and 70.5% and 69.9%, respectively. The increase of sensitivity of the TWIST protocol was statistically significant compared to T&T-IS. CONCLUSION: This study suggests that artificial neural networks may be taken into consideration as a potential clinical decision-support tool for identifying ABG patients at risk for harbouring an unknown thyroid disease and thus requiring diagnostic work-up of their thyroid status.
AIM: To investigate the role of artificial neural networks in predicting the presence of thyroid disease in atrophic body gastritispatients. METHODS: A dataset of 29 input variables of 253 atrophic body gastritispatients was applied to artificial neural networks (ANNs) using a data optimisation procedure (standard ANNs, T&T-IS protocol, TWIST protocol). The target variable was the presence of thyroid disease. RESULTS: Standard ANNs obtained a mean accuracy of 64.4% with a sensitivity of 69% and a specificity of 59.8% in recognizing atrophic body gastritispatients with thyroid disease. The optimization procedures (T&T-IS and TWIST protocol) improved the performance of the recognition task yielding a mean accuracy, sensitivity and specificity of 74.7% and 75.8%, 78.8% and 81.8%, and 70.5% and 69.9%, respectively. The increase of sensitivity of the TWIST protocol was statistically significant compared to T&T-IS. CONCLUSION: This study suggests that artificial neural networks may be taken into consideration as a potential clinical decision-support tool for identifying ABG patients at risk for harbouring an unknown thyroid disease and thus requiring diagnostic work-up of their thyroid status.
Authors: U Feldt-Rasmussen; K Bech; H Bliddal; M Høier-Madsen; F Jørgensen; E Kappelgaard; H Nielsen; J Lanng Nielsen; L P Ryder; M Thomsen Journal: Tissue Antigens Date: 1983-11
Authors: Josceli Maria Tenório; Anderson Diniz Hummel; Vera Lucia Sdepanian; Ivan Torres Pisa; Heimar de Fátima Marin Journal: J Health Inform Date: 2011 Jan-Mar
Authors: Josceli Maria Tenório; Anderson Diniz Hummel; Frederico Molina Cohrs; Vera Lucia Sdepanian; Ivan Torres Pisa; Heimar de Fátima Marin Journal: Int J Med Inform Date: 2011-09-13 Impact factor: 4.046
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