1.
Danese, S, Fiocchi, C. Etiopathogenesis of inflammatory bowel diseases. World J Gastroenterol 2006; 12: 4807–4812.
Google Scholar |
Crossref |
Medline |
ISI2.
Molodecky, NA, Soon, IS, Rabi, DM, et al. Increasing incidence and prevalence of the inflammatory bowel diseases with time, based on systematic review. Gastroenterology 2012; 142: 46–54.e42.
Google Scholar |
Crossref |
Medline3.
Ng, WK, Wong, SH, Ng, SC. Changing epidemiological trends of inflammatory bowel disease in Asia. Intest Res 2016; 14: 111–119.
Google Scholar |
Crossref |
Medline4.
Greenstein, AJ, Sachar, DB, Greenstein, RJ, et al. Intraabdominal abscess in Crohn’s (ileo) colitis. Am J Surg 1982; 143: 727–730.
Google Scholar |
Crossref |
Medline |
ISI5.
Yamaguchi, A, Matsui, T, Sakurai, T, et al. The clinical characteristics and outcome of intraabdominal abscess in Crohn’s disease. J Gastroenterol 2004; 39: 441–448.
Google Scholar |
Crossref |
Medline6.
Keighley, MRB, Eastwood, D, Ambrose, NS, et al. Incidence and microbiology of abdominal and pelvic abscess in Crohn’s disease. Gastroenterology 1982; 83: 1271–1275.
Google Scholar |
Crossref |
Medline |
ISI7.
Poritz, LS, Koltun, WA. Percutaneous drainage and ileocolectomy for spontaneous intraabdominal abscess in Crohn’s disease. J Gastrointest Surg 2007; 11: 204–208.
Google Scholar |
Crossref |
Medline8.
Louis, E, Collard, A, Oger, AF, et al. Behaviour of Crohn’s disease according to the Vienna classification: changing pattern over the course of the disease. Gut 2001; 49: 777–782.
Google Scholar |
Crossref |
Medline |
ISI9.
Agrawal, A, Durrani, S, Leiper, K, et al. Effect of systemic corticosteroid therapy on risk for intra-abdominal or pelvic abscess in non-operated Crohn’s disease. Clin Gastroenterol Hepatol 2005; 3: 1215–1220.
Google Scholar |
Crossref |
Medline |
ISI10.
Picco, M. Tobacco consumption and disease duration are associated with fistulizing and stricturing behaviors in the first 8 years of Crohn’s disease. Am J Gastroenterol 2003; 98: 363–368.
Google Scholar |
Crossref |
Medline11.
Huang, W, Tang, Y, Nong, L, et al. Risk factors for postoperative intra-abdominal septic complications after surgery in Crohn’s disease: a meta-analysis of observational studies. J Crohns Colitis 2015; 9: 293–301.
Google Scholar |
Crossref |
Medline12.
Govani, SM, Guentner, AS, Waljee, AK, et al. Risk stratification of emergency department patients with Crohn’s disease could reduce computed tomography use by nearly half. Clin Gastroenterol Hepatol 2014; 12: 1702–1707.e3.
Google Scholar |
Crossref |
Medline13.
Chatu, S, Subramanian, V, Pollok, RCG. Meta-analysis: diagnostic medical radiation exposure in inflammatory bowel disease. Aliment Pharmacol Ther 2012; 35: 529–539.
Google Scholar |
Crossref |
Medline |
ISI14.
Brenner, DJ, Doll, R, Goodhead, DT, et al. Cancer risks attributable to low doses of ionizing radiation: assessing what we really know. Proc Natl Acad Sci USA 2003; 100(Suppl. 2): 13761–13766.
Google Scholar |
Crossref |
Medline15.
Newnham, E, Hawkes, E, Surender, A, et al. Quantifying exposure to diagnostic medical radiation in patients with inflammatory bowel disease: are we contributing to malignancy? Aliment Pharmacol Ther 2007; 26: 1019–1024.
Google Scholar |
Crossref |
Medline |
ISI16.
Klang, E. Deep learning and medical imaging. J Thorac Dis 2018; 10: 1325–1328.
Google Scholar |
Crossref |
Medline17.
Kourou, K, Exarchos, TP, Exarchos, KP, et al. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J 2015; 13: 8–17.
Google Scholar |
Crossref |
Medline18.
Barash, Y, Soffer, S, Grossman, E, et al. Alerting on mortality among patients discharged from the emergency department: a machine learning model. Postgrad Med J. Epub ahead of print 3 December 2020. DOI:
10.1136/postgradmedj-2020-138899. Google Scholar |
Crossref19.
Klug, M, Barash, Y, Bechler, S, et al. A gradient boosting machine learning model for predicting early mortality in the emergency department triage: devising a nine-point triage score. J Gen Intern Med 2020; 35: 220–227.
Google Scholar |
Crossref |
Medline20.
Klang, E, Kummer, BR, Dangayach, NS, et al. Predicting adult neuroscience intensive care unit admission from emergency department triage using a retrospective, tabular-free text machine learning approach. Sci Rep 2021; 11: 1381.
Google Scholar |
Crossref21.
Bradley, AP. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit 1997; 30: 1145–1159.
Google Scholar |
Crossref |
ISI22.
von Elm, E, Altman, DG, Egger, M, et al. Strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. BMJ 2007; 335: 806–808.
Google Scholar |
Crossref |
Medline23.
Israeli, E, Ying, S, Henderson, B, et al. The impact of abdominal computed tomography in a tertiary referral centre emergency department on the management of patients with inflammatory bowel disease. Aliment Pharmacol Ther 2013; 38: 513–521.
Google Scholar |
Crossref |
Medline24.
Jeong, S-H, Choi, JS, Kim, JW, et al. Clinical features of intra-abdominal abscess and intestinal free-wall perforation in Korean patients with Crohn’s disease: results from the CONNECT study. J Clin Med 2020; 10: 116.
Google Scholar |
Crossref25.
Yarur, AJ, Mandalia, AB, Dauer, RM, et al. Predictive factors for clinically actionable computed tomography findings in inflammatory bowel disease patients seen in the emergency department with acute gastrointestinal symptoms. J Crohns Colitis 2014; 8: 504–512.
Google Scholar |
Crossref |
Medline26.
Yoneno, K, Hisamatsu, T, Matsuoka, K, et al. Risk and management of intra-abdominal abscess in Crohn’s disease treated with infliximab. Digestion 2014; 89: 201–208.
Google Scholar |
Crossref |
Medline27.
Desmond, AN, O’Regan, K, Malik, N, et al. Selection of symptomatic patients with Crohn’s disease for abdominopelvic computed tomography: role of serum C-reactive protein. Can Assoc Radiol J 2012; 63: 267–274.
Google Scholar |
SAGE Journals28.
Govani, SM, Waljee, AK, Kocher, KE, et al. Validation of a tool predicting important findings on computed tomography among Crohn’s disease patients. United European Gastroenterol J 2017; 5: 270–275.
Google Scholar |
SAGE Journals |
ISI29.
Jung, YS, Park, DI, Hong, SN, et al. Predictors of urgent findings on abdominopelvic CT in patients with Crohn’s disease presenting to the emergency department. Dig Dis Sci 2015; 60: 929–935.
Google Scholar |
Crossref |
Medline30.
Kerner, C, Carey, K, Baillie, C, et al. Clinical predictors of urgent findings on abdominopelvic CT in emergency department patients with Crohn’s disease. Inflamm Bowel Dis 2013; 19: 1179–1185.
Google Scholar |
Crossref |
Medline |
ISI31.
Khoury, T, Daher, S, Massarwa, M, et al. A validated score assessing the risk of an intra-abdominal abscess in patients with Crohn’s disease presenting at the emergency department. J Crohns Colitis 2019; 13: 1131–1137.
Google Scholar |
Crossref |
Medline32.
Konikoff, T, Goren, I, Yalon, M, et al. Machine learning for selecting patients with Crohn’s disease for abdominopelvic computed tomography in the emergency department: AI selects CD patients for CT. Dig Liver Dis. Epub ahead of print 9 July 2021. DOI:
10.1016/j.dld.2021.06.020. Google Scholar |
Crossref |
Medline33.
Dignass, A, Van Assche, G, Lindsay, JO, et al. The second European evidence-based consensus on the diagnosis and management of Crohn’s disease: current management. J Crohns Colitis 2010; 4: 28–62.
Google Scholar |
Crossref |
Medline
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