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23篇 您的检索式:作者名="Buscema"
    题名 作者 年代 出处 被引量
1The effect of the substrate on the Raman and photoluminescence emission of single-layer MoS2显示文摘Michele Buscema Gary A. Steele Herre S. J. van der Zant &ndres Castellanos-Gomez 2014Nano Research2014,7,4:28
2Artifi cial neural networks in the recognition of the presence of thyroid disease in patients with atrophic body gastritis显示文摘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.Edith Lahner Marco Intraligi Massimo Buscema Marco Centanni Lucy Vannella Enzo Grossi Bruno Annibale 2008World Journal of Gastroenterology2008,14,4:6
3Possible contribution of artificial neural networks and linear discriminant analysis in recognition of patients with suspected atrophic body gastritis显示文摘AIM: To investigate whether ANNs and LDA could recognize patients with ABG in a database, containing only clinical and biochemical variables, of a pool of patients with and without ABG, by selecting the most predictive variables and by reducing input data to the minimum.METHODS: Data was collected from 350 consecutive outpatients (263 with ABG, 87 with non-atrophic gastritis and/or celiac disease [controls]). Structured questionnaires with 22 items (anagraphic, anamnestic, clinical, and biochemical data) were filled out for each patient. All patients underwent gastroscopy with biopsies. ANNs and LDA were applied to recognize patients with ABG.Experiment 1: random selection on 37 variables, experiment 2: optimization process on 30 variables, experiment 3:input data reduction on 8 variables, experiment 4: use of only clinical input data on 5 variables, and experiment 5:use of only serological variables.RESULTS: In experiment 1, overall accuracies of ANNs and LDA were 96.6% and 94.6%, respectively, for predicting patients with ABG. In experiment 2, ANNs and LDA reached an overall accuracy of 98.8% and 96.8%,respectively. In experiment 3, overall accuracy of ANNs was 98.4%. In experiment 4, overall accuracies of ANNs and LDA were, respectively, 91.3% and 88.6%. In experiment 5, overall accuracies of ANNs and LDA were,respectively, 97.7% and 94.5%.CONCLUSION: This preliminary study suggests that advanced statistical methods, not only ANNs, but also LDA,may contribute to better address bioptic sampling during gastroscopy in a subset of patients in whom ABG may be suspected on the basis of aspecific gastrointestinal symptoms or non-digestive disorders.Edith Lahner Enzo Grossi Marco Intraligi Massimo Buscema Vito D Corleto Gianfranco Delle Fave Bruno Annibale 2005World Journal of Gastroenterology2005,11,37:4
4Elaboration and characterization of thin flims of TiO2 prepared by sol - gel process 显示文摘Legrand- Buscema C Malibert C Bach S 2002Thin Solid Films2002,418,2:1
5Elaboration and characterization of thin films of TiO2 prepared by sol-gel process显示文摘Buscema C L Malibert C Bach S 2002Thin Solid Films2002,418,:1
6Recognition of patients with cardiovascular disease by artificial neural networks 显示文摘BALDASSARRE D GROSSI E BUSCEMA M 2004Ann Med2004,36,8:1
7Artificial neural networks and artificial organisms can predict Alzheimer pathology in individual patients only on the basis of cognitive and functional status 显示文摘Buscema M Grossi E Snowdon D 2004Neuroinformatics2004,2,4:1
8Elaboration and characterization of thin film of TiO2 prepared by sol - gel process显示文摘Legrand- Buscema C Malibert C Bach S 2002Thin Solid Film2002,418,:1
9Dermatofibrosarcoma protuberans of the vulva显示文摘Leake JF Buscema J Cho KR 1991Gynecol Oncol1991,41,3:1
10Regional sensory and chemical characteristics of Malbec wines from Mendoza and California显示文摘King E S Stoumen M Buscema F 0,,:1
11The smart library architecture of an orientation portal显示文摘Buscema M Terzi S Maurelli G Capriotti M Car- lei 2006Quality & Quantity2006,40,6:1
12Introduction to artificial neural networks显示文摘Grossi E Buscema M 2007Eur J Gastroenterol Hepatol2007,19,12:1
13Placental site trophoblstic tumor in a postmenopausal women显示文摘McClellan R Buscema J Currie JL 0,,:1
14Combining personality traits with traditional risk factors for coronary stenosis:An artificial neural networks solution in patients with computed tomography detected coronary artery disease显示文摘Compare A Grossi E Buscema M 0,,:1
15Pleural and parenchy- real pulmonary endometriosis显示文摘Foster D C Stern J L Buscema J 1981Obstet Gynecol1981,58,5:1
16The predominance ofhuman papillomavirus type 16 in vulvar neoplasia 显示文摘Buscema J Naghashfar Z Sawada E 1988Obstet Gynecol1988,71,:1
17The predominance of human papillomavirus type 16 in vulvar neoplasia显示文摘Buscema J Naghashfar E Sawada E 0,,:1
18Elaboration and characterization of thin films of TiO2 prepared by sol-gel process 显示文摘LEGRAND BUSCEMA C MALIBERT C BACH S 2002Thin Solid Films2002,418,:1
19The Smart Library Architecture of an Orientation Portal 显示文摘Buscema M 2006Quality & Quantity-QUAL QUANT2006,40,6:1
20E- laboration and characterization of thin films of TiO2 prepared by sol - gel process 显示文摘Buscema C L Malibert C Bach S 2002Thin Solid Films2002,418,:1
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