Artificial neural network in the mortality prediction for patients in geriatric intensive care unit
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Key words:artificial neural network model  acute physiology and chronic health evaluation Ⅱ  elderly  intensive care unit  prognosis  
Author NameAffiliation
ZHOU WeiWei, et al  
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      Objective To compare the ability of artificial neural networks(ANN) and the acute physiology and chronic health evaluation Ⅱ (APACHE Ⅱ) to predict mortality for patients in geriatric intensive care unit (GICU). Methods The purpose of this retrospective case series was to compare ANN and APACHE Ⅱ in the mortality pridiction for a cohort of patients admitted to a seven-bed GICU in a Beijing teaching general hospital. All 177 patients were older than 65 years and consecutively admitted to our GICU from Jan 2005 to Dec 2006.The 22 variables used to obtain APACHE Ⅱ score and risk of death were collected from each patient on admission. All data were randomly allocated to either the training (n=117) or validation set (n=60). Three ANN models were developed using the data from the training set, namely ANN22 (trained with all the 22 variables), ANN10 (trained with the 10 highest information gain variables) and ANN8 (trained with the 8 highest information gain variables). Three ANN models and APACHE Ⅱwere used to predict mortality in the validation set. The accuracy of ANN and APACHE Ⅱ was assessed by area under the receiver operator characteristics curve (aROC). Results The aROC was 0.943 for ANN22 and 0.949 for APACHE Ⅱin predicting GICU mortality(P=0.829). For ANN10 and ANN8, the aROC was 0.968 and 0.926, respectively. Conclusion Both ANN and APACHE Ⅱhave similar performance in predicting GICU outcome. ANN uses fewer variables and yet is comparable to APACHE Ⅱ.