Differentiating Crohn’s disease from intestinal tuberculosis using a fusion correlation neural network
Tài liệu tham khảo
Watermeyer, 2018, Differentiating Crohn’s disease from intestinal tuberculosis at presentation in patients with tissue granulomas, Samj South Afr. Med. J., 108, 399, 10.7196/SAMJ.2018.v108i5.13108
Meng, 2019, Analysis of phenotypic variables and differentiation between untypical Crohn’s disease and untypical intestinal tuberculosis, Dig. Dis. Sci., 64, 1967, 10.1007/s10620-019-05491-z
Taylor, 1945, Chronic hypertrophic ileocaecal tuberculosis, and its relation to regional ileitis (Crohn’s disease), Br. J. Surg., 33, 178, 10.1002/bjs.18003313017
Cattell, 1946, The surgical treatment of tuberculosis of the bowel, Lahey Clin. Bull., 5, 6
Warren, 1948, Cicatrizing enteritis as a pathologic entity; analysis of 120 cases, Am. J. Pathol., 24, 475
Hoon, 1950, Ileocecal tuberculosis including a comparison of this disease with nonspecific regional enterocolitis and noncaseous tuberculated enterocolitis, Int. Abstr. Surg., 91, 417
Brenner, 1970, Tuberculous colitis simulating nonspecific granulomatous disease of the colon, Am. J. Dig. Dis., 15, 85, 10.1007/BF02239351
Nikolaus, 2007, Diagnostics of inflammatory bowel disease, Gastroenterology, 133, 1670, 10.1053/j.gastro.2007.09.001
Almadi, 2009, Differentiating intestinal tuberculosis from Crohn’s disease: A diagnostic challenge, Am. J. Gastroenterol., 104, 1003, 10.1038/ajg.2008.162
Donoghue, 2009, Intestinal tuberculosis, Curr. Opin. Infect. Dis., 22, 490, 10.1097/QCO.0b013e3283306712
Limsrivilai others, 2017, Meta-analytic Bayesian model for differentiating intestinal tuberculosis from Crohn’s disease, Am. J. Gastroenterol., 112, 415, 10.1038/ajg.2016.529
Limsrivilai, 2016, Meta-analytic Bayesian model for differentiating intestinal tuberculosis from Crohn’s disease utilising clinical, endoscopic, and cross-sectional imaging findings, and the interferon-gamma releasing assay, J. Crohns Colitis, 10, S225, 10.1093/ecco-jcc/jjw019.383
Kirsch, 2006, Role of colonoscopic biopsy in distinguishing between Crohn’s disease and intestinal tuberculosis, J. Clin. Pathol., 59, 840, 10.1136/jcp.2005.032383
Dutta, 2011, Distinguishing Crohn’s disease from intestinal tuberculosis–a prospective study, Trop. Gastroenterol. : Official J. Dig. Dis. Found., 32, 204
Yu, 2012, Clinical, endoscopic and histological differentiations between Crohn’s disease and intestinal tuberculosis, Digestion, 85, 202, 10.1159/000335431
Lei, 2013, Utility of in vitro interferon-gamma release assay in differential diagnosis between intestinal tuberculosis and Crohn’s disease, J. Dig. Dis., 14, 68, 10.1111/1751-2980.12017
Amarapurkar, 2008, Diagnosis of Crohn’s disease in India where tuberculosis is widely prevalent, World J. Gastroenterol., 14, 741, 10.3748/wjg.14.741
Ramadass, 2010, Fecal polymerase chain reaction for Mycobacterium tuberculosis IS6110 to distinguish Crohn’s disease from intestinal tuberculosis, Indian J. Gastroenterol.: Official J. Indian Soc. Gastroenterol., 29, 152, 10.1007/s12664-010-0022-3
Jin, 2010, Histopathology and TB-PCR kit analysis in differentiating the diagnosis of intestinal tuberculosis and Crohn’s disease, World J. Gastroenterol., 16, 2496, 10.3748/wjg.v16.i20.2496
Makharia, 2010, Clinical, endoscopic, and histological differentiations between Crohn’s disease and intestinal tuberculosis, Am. J. Gastroenterol., 105, 642, 10.1038/ajg.2009.585
Fei, 2014, Fluorescent quantitative PCR of Mycobacterium tuberculosis for differentiating intestinal tuberculosis from Crohn’s disease, Braz. J. Med. Biol. Res., 47, 166, 10.1590/1414-431X20133277
Limsrivilai, 2021, Intestinal tuberculosis or Crohn’s disease: a review of the diagnostic models designed to differentiate between these two gastrointestinal diseases, Intest. Res., 19, 21, 10.5217/ir.2019.09142
Ooi, 2016, Asia Pacific consensus statements on Crohn’s disease. Part 1: Definition, diagnosis, and epidemiology (Asia Pacific Crohn’s disease consensus-Part 1), J. Gastroenterol. Hepatol., 31, 45, 10.1111/jgh.12956
Banerjee, 2018, Risk factors for diagnostic delay in Crohn’s disease and their impact on long-term complications: how do they differ in a tuberculosis endemic region?, Aliment. Pharmacol. Ther., 47, 1367, 10.1111/apt.14617
Sharma, 2018, Letter: mucosal response in discriminating intestinal tuberculosis from Crohn’s disease-when to look for it?, Aliment. Pharmacol. Ther., 47, 859, 10.1111/apt.14495
Mouli, 2017, Endoscopic and clinical responses to anti-tubercular therapy can differentiate intestinal tuberculosis from Crohn’s disease, Aliment. Pharmacol. Ther., 45, 27, 10.1111/apt.13840
Lee, 2006, Analysis of colonoscopic findings in the differential diagnosis between intestinal tuberculosis and Crohn’s disease, Endoscopy, 38, 592, 10.1055/s-2006-924996
Yang, 2007, Epidemiology of inflammatory bowel disease in the Songpa-Kangdong District, Seoul, Korea, 1986–2005: A Kasid study, Gastroenterology, 132, A660
Pulimood, 2011, Differentiation of Crohn’s disease from intestinal tuberculosis in India in 2010, World J. Gastroenterol., 17, 433, 10.3748/wjg.v17.i4.433
Tandon, 1972, Pathology of intestinal tuberculosis and its distinction from Crohn’s disease, Gut, 13, 260, 10.1136/gut.13.4.260
Li, 2011, Predictors of clinical and endoscopic findings in differentiating Crohn’s disease from intestinal tuberculosis, Dig. Dis. Sci., 56, 188, 10.1007/s10620-010-1231-4
Kedia, 2015, Computerized tomography-based predictive model for differentiation of Crohn’s disease from intestinal tuberculosis, Indian J. Gastroenterol., 34, 135, 10.1007/s12664-015-0550-y
Jung, 2016, Predictive factors for differentiating between Crohn’s disease and intestinal tuberculosis in Koreans, Am. J. Gastroenterol., 111, 1156, 10.1038/ajg.2016.212
Jung, 2016, Predictive factors for differentiating between Crohn’s disease and intestinal tuberculosis in Korean, Gastroenterology, 150, S552, 10.1016/S0016-5085(16)31889-3
Bae, 2017, Development and validation of a novel prediction model for differential diagnosis between Crohn’s disease and intestinal tuberculosis, Inflamm. Bowel Dis., 23, 1614, 10.1097/MIB.0000000000001162
Wu, 2018, Diagnostic performance of a 5-marker predictive model for differential diagnosis between intestinal tuberculosis and Crohn’s disease, Inflamm. Bowel Dis., 24, 2452, 10.1093/ibd/izy154
He, 2019, Development and validation of a novel diagnostic nomogram to differentiate between intestinal tuberculosis and crohn’s disease: A 6-year prospective multicenter study, Am. J. Gastroenterol., 114, 490, 10.14309/ajg.0000000000000064
Jordan, 2015, Machine learning: Trends, perspectives, and prospects, Science, 349, 255, 10.1126/science.aaa8415
Esteva, 2017, Dermatologist-level classification of skin cancer with deep neural networks, Nature, 542, 115, 10.1038/nature21056
Butler, 2018, Machine learning for molecular and materials science, Nature, 559, 547, 10.1038/s41586-018-0337-2
Chen, 2021, Numerical solving of the generalized Black–Scholes differential equation using Laguerre neural network, Digit. Signal Process., 112, 10.1016/j.dsp.2021.103003
Chen, 2022, Prediction of safety parameters of pressurized water reactor based on feature fusion neural network, Ann. Nucl. Energy, 166, 10.1016/j.anucene.2021.108803
Chen, 2021, Research on users’ participation mechanisms in virtual tourism communities by Bayesian network, Knowl.-Based Syst., 226, 10.1016/j.knosys.2021.107161
Strohmaier, 2020, Ontology, neural networks, and the social sciences, Synthese
Zeng, 1999, Prediction and classification with neural network models, Sociol. Methods Res., 27, 499, 10.1177/0049124199027004002
DeTienne, 2003, Neural networks as statistical tools for business researchers, Organ. Res. Methods, 6, 236, 10.1177/1094428103251907
Weng, 2021, Volatility forecasting of crude oil futures based on a genetic algorithm regularization online extreme learning machine with a forgetting factor: The role of news during the COVID-19 pandemic, Resour. Policy, 73, 10.1016/j.resourpol.2021.102148
Silver, 2016, Mastering the game of Go with deep neural networks and tree search, Nature, 529, 484, 10.1038/nature16961
He, 2016, Deep residual learning for image recognition, 770
Chen, 2020, A deep residual compensation extreme learning machine and applications, J. Forecast., 39, 986, 10.1002/for.2663
Weng, 2020, Gold price forecasting research based on an improved online extreme learning machine algorithm, J. Ambient Intell. Humaniz. Comput., 11, 4101, 10.1007/s12652-020-01682-z
Gulshan others, 2016, Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs, Jama-J. Am. Med. Assoc., 316, 2402, 10.1001/jama.2016.17216
Hosny, 2018, Artificial intelligence in radiology, Nat. Rev. Cancer, 18, 500, 10.1038/s41568-018-0016-5
Hannun, 2019, Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network, Nat. Med., 25, 65, 10.1038/s41591-018-0268-3
Yang, 2020, Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions, J. Thorac. Dis., 12, 165, 10.21037/jtd.2020.02.64
Zhang, 2021, A novel voting convergent difference neural network for diagnosing breast cancer, Neurocomputing, 437, 339, 10.1016/j.neucom.2021.01.083
Hara, 2015, Analysis of function of rectified linear unit used in deep learning
Li, 2018, Improving deep neural network with multiple parametric exponential linear units, Neurocomputing, 301, 11, 10.1016/j.neucom.2018.01.084
Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324
Khedr, 2016, Enhancing iterative dichotomiser 3 algorithm for classification decision tree, Wiley Interdiscip. Rev.-Data Min. Knowl. Discov., 6, 70, 10.1002/widm.1177
Jiang, 2008, A combined classification algorithm based on C4.5 and NB, vol. 5370, 350
Zhang, 2016, C4.5 or Naive Bayes: A discriminative model selection approach, vol. 9886, 419
Teixeira, 2004, Classification and regression tree, Rev. Des. Mal. Respir., 21, 1174, 10.1016/S0761-8425(04)71596-X
N. Cohen-Shapira, L. Rokach, B. Shapira, G. Katz, R. Vainshtein, Acm, AutoGRD: Model recommendation through graphical dataset representation, in: Proceedings of the 28th Acm International Conference on Information & Knowledge Management, 2019, pp. 821–830.
Cao, 2021, Correlation-driven framework based on graph convolutional network for clinical disease classification, J. Stat. Comput. Simul., 9, 1
Jhobta, 2006, Spectrum of perforation peritonitis in India–review of 504 consecutive cases, World J. Emerg. Surg.: WJES, 1, 10.1186/1749-7922-1-26
Yadav, 2013, Spectrum of perforation peritonitis in Delhi: 77 cases experience, Indian J. Surg., 75, 133, 10.1007/s12262-012-0609-2