BACKGROUND: The limitations of current prognostic models in identifying postoperative cardiac patients at risk of experiencing morbidity and subsequently an extended intensive care unit length of stay (ICU LOS) is well recognized. This coupled with the desire for risk stratification in order to prioritize medical intervention has lead to the need for the development of a system that can accurately predict individual patient outcome based on both preoperative and immediate postoperative clinical factors. The usefulness of artificial neural networks (ANNs) as an outcome prediction tool in the critical care environment has been previously demonstrated for medical intensive care unit (ICU) patients and it is the aim of this study to apply this methodology to postoperative cardiac patients. METHODS: A review of contemporary literature revealed 15 preoperative risk factors and 17 operative and postoperative variables that have a determining effect on LOS. An integrated, multi-functional software package was developed to automate the ANN development process. The efficacy of the resultant individual ANNs as well as groupings or ensembles of ANNs were measured by calculating sensitivity and specificity estimates as well as the area under the receiver operating curve (AUC) when the ANN is applied to an independent test dataset. RESULTS: The individual ANN with the highest discriminating ability produced an AUC of 0.819. The use of the ensembles of networks technique significantly improved the classification accuracy. Consolidating the output of three ANNs improved the AUC to 0.90. CONCLUSIONS: This study demonstrates the suitability of ANNs, in particular ensembles of ANNs, to outcome prediction tasks in postoperative cardiac patients.
BACKGROUND: The limitations of current prognostic models in identifying postoperative cardiac patients at risk of experiencing morbidity and subsequently an extended intensive care unit length of stay (ICU LOS) is well recognized. This coupled with the desire for risk stratification in order to prioritize medical intervention has lead to the need for the development of a system that can accurately predict individual patient outcome based on both preoperative and immediate postoperative clinical factors. The usefulness of artificial neural networks (ANNs) as an outcome prediction tool in the critical care environment has been previously demonstrated for medical intensive care unit (ICU) patients and it is the aim of this study to apply this methodology to postoperative cardiac patients. METHODS: A review of contemporary literature revealed 15 preoperative risk factors and 17 operative and postoperative variables that have a determining effect on LOS. An integrated, multi-functional software package was developed to automate the ANN development process. The efficacy of the resultant individual ANNs as well as groupings or ensembles of ANNs were measured by calculating sensitivity and specificity estimates as well as the area under the receiver operating curve (AUC) when the ANN is applied to an independent test dataset. RESULTS: The individual ANN with the highest discriminating ability produced an AUC of 0.819. The use of the ensembles of networks technique significantly improved the classification accuracy. Consolidating the output of three ANNs improved the AUC to 0.90. CONCLUSIONS: This study demonstrates the suitability of ANNs, in particular ensembles of ANNs, to outcome prediction tasks in postoperative cardiac patients.
Authors: José Carlos Ferrão; Mónica Duarte Oliveira; Filipe Janela; Henrique M G Martins Journal: Appl Clin Inform Date: 2016-12-07 Impact factor: 2.342
Authors: Rocco J LaFaro; Suryanarayana Pothula; Keshar Paul Kubal; Mario Emil Inchiosa; Venu M Pothula; Stanley C Yuan; David A Maerz; Lucresia Montes; Stephen M Oleszkiewicz; Albert Yusupov; Richard Perline; Mario Anthony Inchiosa Journal: PLoS One Date: 2015-12-28 Impact factor: 3.240
Authors: Joeri Ruyssinck; Joachim van der Herten; Rein Houthooft; Femke Ongenae; Ivo Couckuyt; Bram Gadeyne; Kirsten Colpaert; Johan Decruyenaere; Filip De Turck; Tom Dhaene Journal: Comput Math Methods Med Date: 2016-10-13 Impact factor: 2.238