A decision support system to facilitate management of patients with acute gastrointestinal bleeding

Rockall, 1995, Incidence of and mortality from acute upper gastrointestinal haemorrhage in the United Kingdom Steering Committee and members of the National Audit of Acute Upper Gastrointestinal Haemorrhage, Br Med J, 311, 222, 10.1136/bmj.311.6999.222 Baradarian, 2004, Early intensive resuscitation of patients with upper gastrointestinal bleeding decreases mortality, Am J Gastroenterol, 99, 619, 10.1111/j.1572-0241.2004.04073.x Elta, 2004, Urgent colonoscopy for acute lower-GI bleeding, Gastrointest Endosc, 59, 402, 10.1016/S0016-5107(03)02721-4 Das, 2004, Prediction of outcome of acute GI hemorrhage: a review of risk scores and predictive models, Gastrointest Endosc, 60, 85, 10.1016/S0016-5107(04)01291-X Das, 2003, Prediction of outcome in acute lower-gastrointestinal haemorrhage based on an artificial neural network: internal and external validation of a predictive model, Lancet, 362, 1261, 10.1016/S0140-6736(03)14568-0 Vapnik, 1995 Tibshirani, 2002, Diagnosis of multiple cancer types by shrunken centroids of gene expression, Proc Natl Acad Sci, 99, 6567, 10.1073/pnas.082099299 Breiman, 1996, Bagging predictors, Mach Learn, 24, 123, 10.1007/BF00058655 Schapire, 1990, The strength of weak learnability, Mach Learn, 5, 197, 10.1007/BF00116037 Breiman, 2001, Random forest, Mach Learn, 45, 5, 10.1023/A:1010933404324 Prakash, 2003, Gastrointest Endosc, 58, 330 Prakash, 2003, Acute small bowel bleeding: a distinct entity with significantly different economic implications compared with GI bleeding from other locations, Gastrointest Endosc, 58, 409, 10.1016/S0016-5107(03)00003-8 Barkun, 2003, Consensus recommendations for managing patients with nonvariceal upper gastrointestinal bleeding, Ann Intern Med, 139, 843, 10.7326/0003-4819-139-10-200311180-00012 Palmer KR. Non-variceal upper gastrointestinal haemorrhage: guidelines. Gut 2002;51(Suppl. 4):iv1–6. Hay, 1997, Prospective evaluation of a clinical guideline recommending hospital length of stay in upper gastrointestinal tract hemorrhage, JAMA, 278, 2151, 10.1001/jama.1997.03550240041031 Hay, 1996, Upper gastrointestinal hemorrhage clinical-guideline determining the optimal hospital length of stay, Am J Med, 100, 313, 10.1016/S0002-9343(97)89490-9 Adler, 2004, ASGE guideline: the role of endoscopy in acute non-variceal upper-GI hemorrhage, Gastrointest Endosc, 60, 497, 10.1016/S0016-5107(04)01568-8 Klebl, 2004, Risk factors for mortality in severe upper gastrointestinal bleeding, Int J Colorectal Dis, 19 Rockall, 1996, Risk assessment after acute upper gastrointestinal haemorrhage, Gut, 38, 316, 10.1136/gut.38.3.316 Rockall, 1996, Selection of patients for early discharge or outpatient care after acute upper gastrointestinal haemorrhage National Audit of Acute Upper Gastrointestinal Haemorrhage, Lancet, 347, 1138, 10.1016/S0140-6736(96)90607-8 Velayos, 2004, Early predictors of severe lower gastrointestinal bleeding and adverse outcomes: a prospective study, Clin Gastroenterol Hepatol, 2, 485, 10.1016/S1542-3565(04)00167-3 Strate, 2003, Early predictors of severity in acute lower intestinal tract bleeding, Arch Intern Med, 163, 838, 10.1001/archinte.163.7.838 Kalula, 2003, Clinical predictors of outcome in acute upper gastrointestinal bleeding, South Afr Med J, 93, 286 Bordley, 1985, Early clinical signs identify low-risk patients with acute upper gastrointestinal hemorrhage, JAMA, 253, 3282, 10.1001/jama.1985.03350460082026 Mortensen, 1994, The diagnostic value of serum urea/creatinine ratio in distinguishing between upper and lower gastrointestinal bleeding A prospective study, Danish Med Bull, 41, 237 Zimmerman, 1995, Predictors of mortality in patients admitted to hospital for acute upper gastrointestinal hemorrhage, Scand J Gastroenterol, 30, 327, 10.3109/00365529509093285 Terdiman, 1997, Risk of persistent or recurrent and intractable upper gastrointestinal bleeding in the era of therapeutic endoscopy, Am J Gastroenterol, 92, 1805 Corley, 1998, Early indicators of prognosis in upper gastrointestinal hemorrhage, Am J Gastroenterol, 93, 336, 10.1111/j.1572-0241.1998.00336.x Blatchford, 2000, A risk score to predict need for treatment for upper-gastrointestinal haemorrhage, Lancet, 356, 1318, 10.1016/S0140-6736(00)02816-6 Zaragoza, 2002, Pre-endoscopic prognostic factors in non-varicose upper gastrointestinal bleeding. Development of a predictive algorithm, Rev Esp Enferm Dig, 94, 139 Molinaro, 2005, Prediction error estimation: a comparison of resampling methods, Bioinformatics, 21, 3301, 10.1093/bioinformatics/bti499 Bamber, 1975, The area above the ordinal dominance graph and the area below the receiver operating graph, J Math Psychol, 12, 387, 10.1016/0022-2496(75)90001-2 Quirk, 1997, Physician specialty and variations in the cost of treating patients with acute upper gastrointestinal bleeding, Gastroenterology, 113, 1443, 10.1053/gast.1997.v113.pm9352845 Timmerman, 1999, Artificial neural network models for the preoperative discrimination between malignant and benign adnexal masses, Ultrasound Obstet Gynecol, 13, 17, 10.1046/j.1469-0705.1999.13010017.x Rosenblatt, 2004, Serum proteomics in cancer diagnosis and management, Annu Rev Med, 55, 97, 10.1146/annurev.med.55.091902.105237 Selaru, 2002, Artificial neural networks distinguish among subtypes of neoplastic colorectal lesions, Gastroenterology, 122, 606, 10.1053/gast.2002.31904 Chong, 2003, Stratification of adverse outcomes by preoperative risk factors in coronary artery bypass graft patients: an artificial neural network prediction model, Proc AMIA Annu Symp, 160 Lund, 2004, Comment on: computerized interpretation of the electrocardiogram, Arch Intern Med, 164, 1698, 10.1001/archinte.164.15.1698-a Kennedy, 1997, An artificial neural network system for diagnosis of acute myocardial infarction (AMI) in the accident and emergency department: evaluation and comparison with serum myoglobin measurements, Comput Methods Prog Biomed, 52, 93, 10.1016/S0169-2607(96)01782-8 Lisboa, 2002, A review of evidence of health benefit from artificial neural networks in medical intervention, Neural Networks, 15, 11, 10.1016/S0893-6080(01)00111-3 Ahn, 2007, Classification by ensembles from random partitions of high-dimensional data, Comput Stat Data Anal, 51, 6166, 10.1016/j.csda.2006.12.043