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For: Perveen S, Shahbaz M, Keshavjee K, Guergachi A. A Systematic Machine Learning Based Approach for the Diagnosis of Non-Alcoholic Fatty Liver Disease Risk and Progression. Sci Rep 2018;8:2112. [PMID: 29391513 DOI: 10.1038/s41598-018-20166-x] [Cited by in Crossref: 19] [Cited by in F6Publishing: 17] [Article Influence: 4.8] [Reference Citation Analysis]
Number Citing Articles
1 Naseem R, Khan B, Shah MA, Wakil K, Khan A, Alosaimi W, Uddin MI, Alouffi B. Performance Assessment of Classification Algorithms on Early Detection of Liver Syndrome. J Healthc Eng 2020;2020:6680002. [PMID: 33489060 DOI: 10.1155/2020/6680002] [Reference Citation Analysis]
2 Książek W, Abdar M, Acharya UR, Pławiak P. A novel machine learning approach for early detection of hepatocellular carcinoma patients. Cognitive Systems Research 2019;54:116-27. [DOI: 10.1016/j.cogsys.2018.12.001] [Cited by in Crossref: 53] [Cited by in F6Publishing: 17] [Article Influence: 17.7] [Reference Citation Analysis]
3 Ahn JC, Connell A, Simonetto DA, Hughes C, Shah VH. Application of Artificial Intelligence for the Diagnosis and Treatment of Liver Diseases. Hepatology 2021;73:2546-63. [PMID: 33098140 DOI: 10.1002/hep.31603] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
4 Popa SL, Ismaiel A, Cristina P, Cristina M, Chiarioni G, David L, Dumitrascu DL. Non-Alcoholic Fatty Liver Disease: Implementing Complete Automated Diagnosis and Staging. A Systematic Review. Diagnostics (Basel) 2021;11:1078. [PMID: 34204822 DOI: 10.3390/diagnostics11061078] [Reference Citation Analysis]
5 Hashida R, Nakano D, Yamamura S, Kawaguchi T, Tsutsumi T, Matsuse H, Takahashi H, Gerber L, Younossi ZM, Torimura T. Association between Activity and Brain-Derived Neurotrophic Factor in Patients with Non-Alcoholic Fatty Liver Disease: A Data-Mining Analysis. Life (Basel) 2021;11:799. [PMID: 34440543 DOI: 10.3390/life11080799] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Oka A, Ishimura N, Ishihara S. A New Dawn for the Use of Artificial Intelligence in Gastroenterology, Hepatology and Pancreatology. Diagnostics (Basel) 2021;11:1719. [PMID: 34574060 DOI: 10.3390/diagnostics11091719] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
7 Li Q, Li JF, Mao XR. Application of artificial intelligence in liver diseases: From diagnosis to treatment. Artif Intell Gastroenterol 2021; 2(5): 133-140 [DOI: 10.35712/aig.v2.i5.133] [Reference Citation Analysis]
8 Spann A, Yasodhara A, Kang J, Watt K, Wang B, Goldenberg A, Bhat M. Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review. Hepatology. 2020;71:1093-1105. [PMID: 31907954 DOI: 10.1002/hep.31103] [Cited by in Crossref: 25] [Cited by in F6Publishing: 27] [Article Influence: 12.5] [Reference Citation Analysis]
9 Kröner PT, Engels MM, Glicksberg BS, Johnson KW, Mzaik O, van Hooft JE, Wallace MB, El-Serag HB, Krittanawong C. Artificial intelligence in gastroenterology: A state-of-the-art review. World J Gastroenterol 2021; 27(40): 6794-6824 [PMID: 34790008 DOI: 10.3748/wjg.v27.i40.6794] [Reference Citation Analysis]
10 Okanoue T, Shima T, Mitsumoto Y, Umemura A, Yamaguchi K, Itoh Y, Yoneda M, Nakajima A, Mizukoshi E, Kaneko S, Harada K. Artificial intelligence/neural network system for the screening of nonalcoholic fatty liver disease and nonalcoholic steatohepatitis. Hepatol Res 2021;51:554-69. [PMID: 33594747 DOI: 10.1111/hepr.13628] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
11 Veerankutty FH, Jayan G, Yadav MK, Manoj KS, Yadav A, Nair SRS, Shabeerali TU, Yeldho V, Sasidharan M, Rather SA. Artificial Intelligence in hepatology, liver surgery and transplantation: Emerging applications and frontiers of research. World J Hepatol 2021; 13(12): 1977-1990 [DOI: 10.4254/wjh.v13.i12.1977] [Reference Citation Analysis]
12 Chocholova E, Bertok T, Jane E, Lorencova L, Holazova A, Belicka L, Belicky S, Mislovicova D, Vikartovska A, Imrich R, Kasak P, Tkac J. Glycomics meets artificial intelligence - Potential of glycan analysis for identification of seropositive and seronegative rheumatoid arthritis patients revealed. Clin Chim Acta 2018;481:49-55. [PMID: 29486148 DOI: 10.1016/j.cca.2018.02.031] [Cited by in Crossref: 11] [Cited by in F6Publishing: 8] [Article Influence: 2.8] [Reference Citation Analysis]
13 Khan B, Naseem R, Shah MA, Wakil K, Khan A, Uddin MI, Mahmoud M. Software Defect Prediction for Healthcare Big Data: An Empirical Evaluation of Machine Learning Techniques. J Healthc Eng 2021;2021:8899263. [PMID: 33815733 DOI: 10.1155/2021/8899263] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 Perveen S, Shahbaz M, Ansari MS, Keshavjee K, Guergachi A. A Hybrid Approach for Modeling Type 2 Diabetes Mellitus Progression. Front Genet 2019;10:1076. [PMID: 31969896 DOI: 10.3389/fgene.2019.01076] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
15 Li Y, Wang X, Zhang J, Zhang S, Jiao J. Applications of artificial intelligence (AI) in researches on non-alcoholic fatty liver disease(NAFLD) : A systematic review. Rev Endocr Metab Disord 2021. [PMID: 34396467 DOI: 10.1007/s11154-021-09681-x] [Reference Citation Analysis]
16 Atsawarungruangkit A, Laoveeravat P, Promrat K. Machine learning models for predicting non-alcoholic fatty liver disease in the general United States population: NHANES database. World J Hepatol 2021; 13(10): 1417-1427 [PMID: 34786176 DOI: 10.4254/wjh.v13.i10.1417] [Reference Citation Analysis]
17 Sorino P, Campanella A, Bonfiglio C, Mirizzi A, Franco I, Bianco A, Caruso MG, Misciagna G, Aballay LR, Buongiorno C, Liuzzi R, Cisternino AM, Notarnicola M, Chiloiro M, Fallucchi F, Pascoschi G, Osella AR. Development and validation of a neural network for NAFLD diagnosis. Sci Rep 2021;11:20240. [PMID: 34642390 DOI: 10.1038/s41598-021-99400-y] [Reference Citation Analysis]
18 Desterke C, Chiappini F. Lipid Related Genes Altered in NASH Connect Inflammation in Liver Pathogenesis Progression to HCC: A Canonical Pathway. Int J Mol Sci 2019;20:E5594. [PMID: 31717414 DOI: 10.3390/ijms20225594] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
19 Perveen S, Shahbaz M, Keshavjee K, Guergachi A. Prognostic Modeling and Prevention of Diabetes Using Machine Learning Technique. Sci Rep 2019;9:13805. [PMID: 31551457 DOI: 10.1038/s41598-019-49563-6] [Cited by in Crossref: 14] [Cited by in F6Publishing: 8] [Article Influence: 4.7] [Reference Citation Analysis]