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Artifi cial neural networks in the recognition of the presence of thyroid disease in patients with atrophic body gastritis

查看全文 作　　者：Edith [1]Lahner;Marco [2]Intraligi;Massimo [2]Buscema;Marco [3]Centanni;Lucy [1]Vannella;Enzo [4]Grossi;Bruno [1]Annibale 高影响力作者 机构地区：[1]Department of Digestive and Liver Disease,University 'La Sapienza',2nd Medical School,Ospedale Sant'Andrea,Rome,Italy;[2]Semeion Research Center,Rome Italy;[3]Endocrinology Unit,Department of Experimental Medicine and Pathology,University 'La Sapienza',Polo Pontino,Latina,Italy;[4]Bracco Imaging Spa,Milan,Italy高影响力机构 出　　处：《World Journal of Gastroenterology》索引2008年第14卷第4期,共6页高影响力期刊 基　　金：funds from MIUR 2005 (Italian Ministry for University and Research) and University Sapienza Roma 摘　　要：AIM: To investigate the role of artifi cial 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 artifi cial 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 specifi city 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 specifi city 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 signifi cant 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. 关 键 词：萎缩性胃炎 甲状腺疾病 人造神经网络 症状
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