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For: Yip TC, Ma AJ, Wong VW, Tse Y, Chan HL, Yuen P, Wong GL. Laboratory parameter-based machine learning model for excluding non-alcoholic fatty liver disease (NAFLD) in the general population. Aliment Pharmacol Ther 2017;46:447-56. [DOI: 10.1111/apt.14172] [Cited by in Crossref: 58] [Cited by in F6Publishing: 50] [Article Influence: 11.6] [Reference Citation Analysis]
Number Citing Articles
1 Wong VW, Adams LA, de Lédinghen V, Wong GL, Sookoian S. Noninvasive biomarkers in NAFLD and NASH — current progress and future promise. Nat Rev Gastroenterol Hepatol 2018;15:461-78. [DOI: 10.1038/s41575-018-0014-9] [Cited by in Crossref: 153] [Cited by in F6Publishing: 144] [Article Influence: 38.3] [Reference Citation Analysis]
2 Cheuk-fung Yip T, Wai-sun Wong V, Lik-yuen Chan H, Tse Y, Pik-shan Kong A, Long-yan Lam K, Chung-yan Lui G, Lai-hung Wong G. Effects of Diabetes and Glycemic Control on Risk of Hepatocellular Carcinoma After Seroclearance of Hepatitis B Surface Antigen. Clinical Gastroenterology and Hepatology 2018;16:765-773.e2. [DOI: 10.1016/j.cgh.2017.12.009] [Cited by in Crossref: 20] [Cited by in F6Publishing: 16] [Article Influence: 5.0] [Reference Citation Analysis]
3 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]
4 Bergram M, Nasr P, Iredahl F, Kechagias S, Rådholm K, Ekstedt M. Low awareness of non-alcoholic fatty liver disease in patients with type 2 diabetes in Swedish Primary Health Care. Scand J Gastroenterol 2021;:1-10. [PMID: 34618619 DOI: 10.1080/00365521.2021.1984572] [Reference Citation Analysis]
5 Zhang C, Yang M. Current Options and Future Directions for NAFLD and NASH Treatment. Int J Mol Sci 2021;22:7571. [PMID: 34299189 DOI: 10.3390/ijms22147571] [Reference Citation Analysis]
6 Tincopa MA. Diagnostic and interventional circulating biomarkers in nonalcoholic steatohepatitis. Endocrinol Diabetes Metab 2020;3:e00177. [PMID: 33102798 DOI: 10.1002/edm2.177] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
7 Wai JW, Fu C, Wong VW. Confounding factors of non-invasive tests for nonalcoholic fatty liver disease. J Gastroenterol 2020;55:731-41. [PMID: 32451628 DOI: 10.1007/s00535-020-01686-8] [Cited by in Crossref: 8] [Cited by in F6Publishing: 7] [Article Influence: 4.0] [Reference Citation Analysis]
8 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]
9 Su TH, Wu CH, Kao JH. Artificial intelligence in precision medicine in hepatology. J Gastroenterol Hepatol 2021;36:569-80. [PMID: 33709606 DOI: 10.1111/jgh.15415] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
10 Wong GL, Chan HL, Tse YK, Yip TC, Lam KL, Lui GC, Wong VW. Normal on-treatment ALT during antiviral treatment is associated with a lower risk of hepatic events in patients with chronic hepatitis B. J Hepatol 2018;69:793-802. [PMID: 29758335 DOI: 10.1016/j.jhep.2018.05.009] [Cited by in Crossref: 41] [Cited by in F6Publishing: 38] [Article Influence: 13.7] [Reference Citation Analysis]
11 Christodoulou E, Ma J, Collins GS, Steyerberg EW, Verbakel JY, Van Calster B. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol. 2019;110:12-22. [PMID: 30763612 DOI: 10.1016/j.jclinepi.2019.02.004] [Cited by in Crossref: 320] [Cited by in F6Publishing: 264] [Article Influence: 106.7] [Reference Citation Analysis]
12 Chan SL, Yip TC, Wong VW, Tse YK, Yuen BW, Luk HW, Lui RN, Chan HL, Mok TS, Wong GL. Pattern and impact of hepatic adverse events encountered during immune checkpoint inhibitors - A territory-wide cohort study. Cancer Med 2020;9:7052-61. [PMID: 32780516 DOI: 10.1002/cam4.3378] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
13 Kanwal F, Shubrook JH, Younossi Z, Natarajan Y, Bugianesi E, Rinella ME, Harrison SA, Mantzoros C, Pfotenhauer K, Klein S, Eckel RH, Kruger D, El-Serag H, Cusi K. Preparing for the NASH epidemic: A call to action. Metabolism 2021;122:154822. [PMID: 34289945 DOI: 10.1016/j.metabol.2021.154822] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
14 Zhao M, Song C, Luo T, Huang T, Lin S. Fatty Liver Disease Prediction Model Based on Big Data of Electronic Physical Examination Records. Front Public Health 2021;9:668351. [PMID: 33912534 DOI: 10.3389/fpubh.2021.668351] [Reference Citation Analysis]
15 Makri E, Kita M, Goulas A, Papaioannidou P, Efstathiadou ZA, Adamidou F, Polyzos SA. Comparative effectiveness of glucagon-like peptide-1 receptor agonists versus dipeptidyl peptidase-4 inhibitors on noninvasive indices of hepatic steatosis and fibrosis in patients with type 2 diabetes mellitus. Diabetes Metab Syndr 2020;14:1913-9. [PMID: 33011499 DOI: 10.1016/j.dsx.2020.09.030] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
16 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]
17 Cai X, Aierken X, Ahmat A, Cao Y, Zhu Q, Wu T, Li N. A Nomogram Model Based on Noninvasive Bioindicators to Predict 3-Year Risk of Nonalcoholic Fatty Liver in Nonobese Mainland Chinese: A Prospective Cohort Study. Biomed Res Int 2020;2020:8852198. [PMID: 33204721 DOI: 10.1155/2020/8852198] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
18 Wong GL, Yuen PC, Ma AJ, Chan AW, Leung HH, Wong VW. Artificial intelligence in prediction of non-alcoholic fatty liver disease and fibrosis. J Gastroenterol Hepatol 2021;36:543-50. [PMID: 33709607 DOI: 10.1111/jgh.15385] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 8.0] [Reference Citation Analysis]
19 Ciardullo S, Muraca E, Perra S, Bianconi E, Zerbini F, Oltolini A, Cannistraci R, Parmeggiani P, Manzoni G, Gastaldelli A, Lattuada G, Perseghin G. Screening for non-alcoholic fatty liver disease in type 2 diabetes using non-invasive scores and association with diabetic complications. BMJ Open Diabetes Res Care. 2020;8. [PMID: 32049637 DOI: 10.1136/bmjdrc-2019-000904] [Cited by in Crossref: 19] [Cited by in F6Publishing: 20] [Article Influence: 19.0] [Reference Citation Analysis]
20 Christou CD, Tsoulfas G. Challenges and opportunities in the application of artificial intelligence in gastroenterology and hepatology. World J Gastroenterol 2021; 27(37): 6191-6223 [PMID: 34712027 DOI: 10.3748/wjg.v27.i37.6191] [Reference Citation Analysis]
21 Ye FZ, Liu WY, Zheng KI, Pan XY, Ma HL, Wang XD, Chen YP, Zheng MH. Homeostatic model assessment of insulin resistance closely related to lobular inflammation in nonalcoholic fatty liver disease. Eur J Gastroenterol Hepatol 2020;32:80-6. [PMID: 31625959 DOI: 10.1097/MEG.0000000000001483] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
22 Wu C, Yeh W, Hsu W, Islam MM, Nguyen PA, Poly TN, Wang Y, Yang H, (Jack) Li Y. Prediction of fatty liver disease using machine learning algorithms. Computer Methods and Programs in Biomedicine 2019;170:23-9. [DOI: 10.1016/j.cmpb.2018.12.032] [Cited by in Crossref: 52] [Cited by in F6Publishing: 26] [Article Influence: 17.3] [Reference Citation Analysis]
23 Kanwal F, Shubrook JH, Younossi Z, Natarajan Y, Bugianesi E, Rinella ME, Harrison SA, Mantzoros C, Pfotenhauer K, Klein S, Eckel RH, Kruger D, El-Serag H, Cusi K. Preparing for the NASH Epidemic: A Call to Action. Gastroenterology 2021;161:1030-1042.e8. [PMID: 34416976 DOI: 10.1053/j.gastro.2021.04.074] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
24 Wu CT, Chu TW, Jang JR. Current-Visit and Next-Visit Prediction for Fatty Liver Disease With a Large-Scale Dataset: Model Development and Performance Comparison. JMIR Med Inform 2021;9:e26398. [PMID: 34387552 DOI: 10.2196/26398] [Reference Citation Analysis]
25 Yu Y, Cai J, She Z, Li H. Insights into the Epidemiology, Pathogenesis, and Therapeutics of Nonalcoholic Fatty Liver Diseases. Adv Sci (Weinh). 2019;6:1801585. [PMID: 30828530 DOI: 10.1002/advs.201801585] [Cited by in Crossref: 34] [Cited by in F6Publishing: 39] [Article Influence: 8.5] [Reference Citation Analysis]
26 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]
27 Sorino P, Caruso MG, Misciagna G, Bonfiglio C, Campanella A, Mirizzi A, Franco I, Bianco A, Buongiorno C, Liuzzi R, Cisternino AM, Notarnicola M, Chiloiro M, Pascoschi G, Osella AR; MICOL Group. Selecting the best machine learning algorithm to support the diagnosis of Non-Alcoholic Fatty Liver Disease: A meta learner study. PLoS One 2020;15:e0240867. [PMID: 33079971 DOI: 10.1371/journal.pone.0240867] [Cited by in Crossref: 3] [Cited by in F6Publishing: 6] [Article Influence: 1.5] [Reference Citation Analysis]
28 Drescher HK, Weiskirchen S, Weiskirchen R. Current Status in Testing for Nonalcoholic Fatty Liver Disease (NAFLD) and Nonalcoholic Steatohepatitis (NASH). Cells 2019;8:E845. [PMID: 31394730 DOI: 10.3390/cells8080845] [Cited by in Crossref: 45] [Cited by in F6Publishing: 42] [Article Influence: 15.0] [Reference Citation Analysis]
29 Ahn Y, Yun SC, Lee SS, Son JH, Jo S, Byun J, Sung YS, Kim HS, Yu ES. Development and Validation of a Simple Index Based on Non-Enhanced CT and Clinical Factors for Prediction of Non-Alcoholic Fatty Liver Disease. Korean J Radiol 2020;21:413-21. [PMID: 32193889 DOI: 10.3348/kjr.2019.0703] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
30 Pan X, Xie X, Peng H, Cai X, Li H, Hong Q, Wu Y, Lin X, Xu S, Peng XE. Risk Prediction for Non-alcoholic Fatty Liver Disease Based on Biochemical and Dietary Variables in a Chinese Han Population. Front Public Health 2020;8:220. [PMID: 32714888 DOI: 10.3389/fpubh.2020.00220] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
31 Chan P, Zhou X, Wang N, Liu Q, Bruno R, Jin JY. Application of Machine Learning for Tumor Growth Inhibition - Overall Survival Modeling Platform. CPT Pharmacometrics Syst Pharmacol 2021;10:59-66. [PMID: 33280255 DOI: 10.1002/psp4.12576] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
32 Becker AK, Dörr M, Felix SB, Frost F, Grabe HJ, Lerch MM, Nauck M, Völker U, Völzke H, Kaderali L. From heterogeneous healthcare data to disease-specific biomarker networks: A hierarchical Bayesian network approach. PLoS Comput Biol 2021;17:e1008735. [PMID: 33577591 DOI: 10.1371/journal.pcbi.1008735] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
33 Aggarwal P, Alkhouri N. Artificial Intelligence in Nonalcoholic Fatty Liver Disease: A New Frontier in Diagnosis and Treatment. Clin Liver Dis (Hoboken) 2021;17:392-7. [PMID: 34386201 DOI: 10.1002/cld.1071] [Reference Citation Analysis]
34 García-Carretero R, Holgado-Cuadrado R, Barquero-Pérez Ó. Assessment of Classification Models and Relevant Features on Nonalcoholic Steatohepatitis Using Random Forest. Entropy (Basel) 2021;23:763. [PMID: 34204225 DOI: 10.3390/e23060763] [Reference Citation Analysis]
35 Alqahtani SA, Schattenberg JM. Nonalcoholic fatty liver disease: use of diagnostic biomarkers and modalities in clinical practice. Expert Rev Mol Diagn 2021;:1-14. [PMID: 34346799 DOI: 10.1080/14737159.2021.1964958] [Reference Citation Analysis]
36 Le Berre C, Sandborn WJ, Aridhi S, Devignes MD, Fournier L, Smaïl-Tabbone M, Danese S, Peyrin-Biroulet L. Application of Artificial Intelligence to Gastroenterology and Hepatology. Gastroenterology 2020;158:76-94.e2. [PMID: 31593701 DOI: 10.1053/j.gastro.2019.08.058] [Cited by in Crossref: 108] [Cited by in F6Publishing: 96] [Article Influence: 36.0] [Reference Citation Analysis]
37 Yip TC, Wong VW, Wong GL. Alanine Aminotransferase Level: The Road to Normal in 2021. Hepatol Commun 2021;5:1807-9. [PMID: 34719129 DOI: 10.1002/hep4.1788] [Reference Citation Analysis]
38 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]
39 Murali N, Kucukkaya A, Petukhova A, Onofrey J, Chapiro J. Supervised Machine Learning in Oncology: A Clinician's Guide. Dig Dis Interv 2020;4:73-81. [PMID: 32869010 DOI: 10.1055/s-0040-1705097] [Cited by in Crossref: 5] [Cited by in F6Publishing: 1] [Article Influence: 2.5] [Reference Citation Analysis]
40 Zhang X, Wong VW, Yip TC, Tse YK, Liang LY, Hui VW, Li GL, Chan HL, Wong GL. Colonoscopy and Risk of Colorectal Cancer in Patients With Nonalcoholic Fatty Liver Disease: A Retrospective Territory-Wide Cohort Study. Hepatol Commun 2021;5:1212-23. [PMID: 34278170 DOI: 10.1002/hep4.1705] [Reference Citation Analysis]
41 Peng K, Wang S, Gao L, You H. A nomogram incorporated lifestyle indicators for predicting nonalcoholic fatty liver disease. Medicine (Baltimore) 2021;100:e26415. [PMID: 34190160 DOI: 10.1097/MD.0000000000026415] [Reference Citation Analysis]
42 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]
43 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]
44 Wong GL, Ma AJ, Deng H, Ching JY, Wong VW, Tse YK, Yip TC, Lau LH, Liu HH, Leung CM, Tsang SW, Chan CW, Lau JY, Yuen PC, Chan FK. Machine learning model to predict recurrent ulcer bleeding in patients with history of idiopathic gastroduodenal ulcer bleeding. Aliment Pharmacol Ther. 2019;49:912-918. [PMID: 30761584 DOI: 10.1111/apt.15145] [Cited by in Crossref: 16] [Cited by in F6Publishing: 16] [Article Influence: 5.3] [Reference Citation Analysis]
45 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]
46 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]
47 Kanwal F, Shubrook JH, Younossi Z, Natarajan Y, Bugianesi E, Rinella ME, Harrison SA, Mantzoros C, Pfotenhauer K, Klein S, Eckel RH, Kruger D, El-Serag H, Cusi K. Preparing for the NASH epidemic: A call to action. Obesity (Silver Spring) 2021;29:1401-12. [PMID: 34365735 DOI: 10.1002/oby.23250] [Reference Citation Analysis]
48 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]
49 Pu X, Deng D, Chu C, Zhou T, Liu J. High-dimensional hepatopath data analysis by machine learning for predicting HBV-related fibrosis. Sci Rep 2021;11:5081. [PMID: 33658585 DOI: 10.1038/s41598-021-84556-4] [Reference Citation Analysis]
50 Yip TC, Wong VW, Wong GL. Liver-heart connection in diabetes mellitus. J Gastroenterol Hepatol 2021;36:1385-6. [PMID: 34105827 DOI: 10.1111/jgh.15533] [Reference Citation Analysis]
51 Wong GL, Wong VW, Yuen BW, Tse YK, Yip TC, Luk HW, Lui GC, Chan HL. Risk of hepatitis B surface antigen seroreversion after corticosteroid treatment in patients with previous hepatitis B virus exposure. J Hepatol. 2020;72:57-66. [PMID: 31499132 DOI: 10.1016/j.jhep.2019.08.023] [Cited by in Crossref: 23] [Cited by in F6Publishing: 20] [Article Influence: 7.7] [Reference Citation Analysis]
52 Tang Z, Pham M, Hao Y, Wang F, Patel D, Jean-Baptiste L, Fan L, Wang W, Wang Y, Cheng F. Sex, Age, and BMI Modulate the Association of Physical Examinations and Blood Biochemistry Parameters and NAFLD: A Retrospective Study on 1994 Cases Observed at Shuguang Hospital, China. Biomed Res Int 2019;2019:1246518. [PMID: 31341886 DOI: 10.1155/2019/1246518] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 1.3] [Reference Citation Analysis]
53 Balsano C, Alisi A, Brunetto MR, Invernizzi P, Burra P, Piscaglia F; Special Interest Group (SIG) Artificial Intelligence and Liver Diseases; Italian Association for the Study of the Liver (AISF). The application of artificial intelligence in hepatology: A systematic review. Dig Liver Dis 2021:S1590-8658(21)00317-0. [PMID: 34266794 DOI: 10.1016/j.dld.2021.06.011] [Reference Citation Analysis]