A neural network model for mortality prediction in ICU

Abstract

Scoring the severity of illness of ICU patients can provide evaluation of a patient's situation and thus help doctors make decisions on what treatment to take. This study aimed to develop an artificial neural network model for patient-specific prediction of in-hospital mortality. Data from PhysioNet Challenge 2012 was used. 12,000 records were divided to a training set, a test set and a validation set, each of which contains 4000 records. Outcomes are provided for the training set. A neural network model was developed to predict the risk of inhospital mortality using various physiological measurements from the ICU. Twenty-six features were selected after a thorough investigation over the different variables and features. A two-layer neural network with fifteen neurons in the hidden layer was used for classification. One hundred voting classifiers were trained and the model's output was the average of the one hundred outputs. A fuzzy threshold was utilized to determine the outcome of each record from the output of the network. Our model yielded an event 1 score of 0.5088 and an event 2 score of 82.211 on the test data set.

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