Mortality assessment in intensive care units via adverse events using artificia...

• Author(s): Silva, Álvaro ; Cortez, Paulo, 1971- ; Santos, Manuel Filipe ; Gomes, Lopes ; Neves, José
• Date: 2006
• Persistent ID: http://hdl.handle.net/1822/5412
• Origin: RepositóriUM - Universidade do Minho
• Subject(s): Artificial neural networks; Classification; Data mining; Intensive care; Logistic regression
• Document Type: Article
• Language: English

Description

This work presents a novel approach for the prediction of mortality in intensive care units (ICUs) based on the use of adverse events, which are defined from four bedside alarms, and artificial neural networks (ANNs). This approach is compared with two logistic regression (LR) models: the prognostic model used in most of the European ICUs, based on the simplified acute physiology score (SAPS II), and a LR that uses the same input variables of the ANN model. Materials and Methods: A large dataset was considered, encompassing forty two ICUs of nine European countries. The recorded features of each patient include the final outcome, the case mix (e.g. age) and the intermediate outcomes, defined as the daily averages of the out of range values of four biometrics (e.g. heart rate). The SAPS II score requires seventeen static variables (e.g. serum sodium), which are collected within the first day of the patient's admission. A nonlinear least squares method was used to calibrate the LR models while the ANNs are made up of multilayer perceptrons trained by the RPROP algorithm. A total of 13164 adult patients were randomly divided into training (66%) and test (33%) sets. The two methods were evaluated in terms of receiver operator characteristic (ROC) curves. Results: The event based models predicted the outcome more accurately than the currently used SAPS II model (P<0.05), with ROC areas within the ranges 83.9-87.1% (ANN) and 82.6-85.2% (LR) vs 80% (LR SAPS II). When using the same inputs, the ANNs outperform the LR (improvement of 1.3-2%). Conclusion: Better prognostic models can be achieved by adopting low cost and real-time intermediate outcomes rather than static data.