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Artificial neural networks as prediction tools in the critically ill.
Clermont G.
Crit Care. 2005 Apr;9(2):153-4. Epub 2005 Mar 3.PMID: 15774070 [PubMed - indexed for MEDLINE]Related articlesFree article
Comparison between logistic regression and neural networks to predict death in patients with suspected sepsis in the emergency room.
Jaimes F, Farbiarz J, Alvarez D, Martínez C.
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Risk factor identification and mortality prediction in cardiac surgery using artificial neural networks.
Nilsson J, Ohlsson M, Thulin L, Höglund P, Nashef SA, Brandt J.
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Artificial neural network models for prediction of acute coronary syndromes using clinical data from the time of presentation.
Harrison RF, Kennedy RL.
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Comparison of artificial neural networks with logistic regression in prediction of gallbladder disease among obese patients.
Liew PL, Lee YC, Lin YC, Lee TS, Lee WJ, Wang W, Chien CW.
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Circulating levels of GH predict mortality and complement prognostic scores in critically ill medical patients.
Schuetz P, Müller B, Nusbaumer C, Wieland M, Christ-Crain M.
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Predicting adverse outcomes of cardiac surgery with the application of artificial neural networks.
Peng SY, Peng SK.
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A comparison of MICU survival prediction using the logistic regression model and artificial neural network model.
Lin SP, Lee CH, Lu YS, Hsu LN.
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Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes.
Tu JV.
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Rifle classification for predicting in-hospital mortality in critically ill sepsis patients.
Chen YC, Jenq CC, Tian YC, Chang MY, Lin CY, Chang CC, Lin HC, Fang JT, Yang CW, Lin SM.
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Support vector machine versus logistic regression modeling for prediction of hospital mortality in critically ill patients with haematological malignancies.
Verplancke T, Van Looy S, Benoit D, Vansteelandt S, Depuydt P, De Turck F, Decruyenaere J.
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Acute and long-term survival in chronically critically ill surgical patients: a retrospective observational study.
Hartl WH, Wolf H, Schneider CP, Küchenhoff H, Jauch KW.
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Estimating long-term survival of critically ill patients: the PREDICT model.
Ho KM, Knuiman M, Finn J, Webb SA.
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Prospective cohort study comparing sequential organ failure assessment and acute physiology, age, chronic health evaluation III scoring systems for hospital mortality prediction in critically ill cirrhotic patients.
Chen YC, Tian YC, Liu NJ, Ho YP, Yang C, Chu YY, Chen PC, Fang JT, Hsu CW, Yang CW, Tsai MH.
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Using neural networks to predict the onset of diabetes mellitus.
Shanker MS.
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The accuracy of artificial neural networks in predicting long-term outcome after traumatic brain injury.
Segal ME, Goodman PH, Goldstein R, Hauck W, Whyte J, Graham JW, Polansky M, Hammond FM.
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Mortality risk factors and validation of severity scoring systems in critically ill patients with acute renal failure.
Lima EQ, Dirce MT, Castro I, Yu L.
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Development of river ecosystem models for Flemish watercourses: case studies in the Zwalm river basin.
Goethals P, Dedecker A, Raes N, Adriaenssens V, Gabriels W, De Pauw N.
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Prediction of significant fibrosis in hepatitis C virus infected liver transplant recipients by artificial neural network analysis of clinical factors.
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