Literature DB >> 10540093

A comparison of ICU mortality prediction using the APACHE II scoring system and artificial neural networks.

L S Wong1, J D Young.   

Abstract

The aim of this study was to compare the ability of artificial neural networks and the Acute Physiology and Chronic Health Evaluation II score to predict mortality in adult intensive care units. The same physiological variables were used in both predictive models to predict hospital mortality from a data set of 8796 patients collected from 26 adult intensive care units in the United Kingdom and Ireland as part of the Intensive Care Society study. The results from the two models were compared with the actual outcome. The overall prediction accuracy and the overall goodness-of-fit of all the models were assessed. Both predictive models showed similar goodness-of-fit and prediction discrimination. The overall predictive and classification performance of the artificial neural network developed matched and in some aspects was better than that of Acute Physiology and Chronic Health Evaluation II.

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Year:  1999        PMID: 10540093     DOI: 10.1046/j.1365-2044.1999.01104.x

Source DB:  PubMed          Journal:  Anaesthesia        ISSN: 0003-2409            Impact factor:   6.955


  14 in total

Review 1.  [Artificial neural networks. Theory and applications in anesthesia, intensive care and emergency medicine].

Authors:  M Traeger; A Eberhart; G Geldner; A M Morin; C Putzke; H Wulf; L H Eberhart
Journal:  Anaesthesist       Date:  2003-11       Impact factor: 1.041

2.  Artificial Intelligence and Machine Learning in Anesthesiology.

Authors:  Christopher W Connor
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3.  A Comparison of Intensive Care Unit Mortality Prediction Models through the Use of Data Mining Techniques.

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Journal:  Healthc Inform Res       Date:  2011-12-31

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Authors:  Farzad Mirzakhani; Farahnaz Sadoughi; Mahboobeh Hatami; Alireza Amirabadizadeh
Journal:  BMC Med Inform Decis Mak       Date:  2022-06-26       Impact factor: 3.298

5.  Prediction of mortality in an Indian intensive care unit. Comparison between APACHE II and artificial neural networks.

Authors:  Ashish Nimgaonkar; Dilip R Karnad; S Sudarshan; Lucila Ohno-Machado; Isaac Kohane
Journal:  Intensive Care Med       Date:  2004-01-15       Impact factor: 17.440

6.  New algorithm of mortality risk prediction for cardiovascular patients admitted in intensive care unit.

Authors:  Mohammad Karimi Moridani; Seyed Kamaledin Setarehdan; Ali Motie Nasrabadi; Esmaeil Hajinasrollah
Journal:  Int J Clin Exp Med       Date:  2015-06-15

7.  Machine Learning and Decision Support in Critical Care.

Authors:  Alistair E W Johnson; Mohammad M Ghassemi; Shamim Nemati; Katherine E Niehaus; David A Clifton; Gari D Clifford
Journal:  Proc IEEE Inst Electr Electron Eng       Date:  2016-01-25       Impact factor: 10.961

8.  A novel approach for prediction of tacrolimus blood concentration in liver transplantation patients in the intensive care unit through support vector regression.

Authors:  Stijn Van Looy; Thierry Verplancke; Dominique Benoit; Eric Hoste; Georges Van Maele; Filip De Turck; Johan Decruyenaere
Journal:  Crit Care       Date:  2007       Impact factor: 9.097

9.  Comparison of machine learning models for the prediction of mortality of patients with unplanned extubation in intensive care units.

Authors:  Meng Hsuen Hsieh; Meng Ju Hsieh; Chin-Ming Chen; Chia-Chang Hsieh; Chien-Ming Chao; Chih-Cheng Lai
Journal:  Sci Rep       Date:  2018-11-20       Impact factor: 4.379

10.  Machine learning in critical care: state-of-the-art and a sepsis case study.

Authors:  Alfredo Vellido; Vicent Ribas; Carles Morales; Adolfo Ruiz Sanmartín; Juan Carlos Ruiz Rodríguez
Journal:  Biomed Eng Online       Date:  2018-11-20       Impact factor: 2.819

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