A neural network for prediction of risk of nosocomial infection at intensive care units: a didactic preliminary model.

ABSTRACT:

Objective

To propose a preliminary artificial intelligence model, based on artificial neural networks, for predicting the risk of nosocomial infection at intensive care units.

Methods

An artificial neural network is designed that employs supervised learning. The generation of the datasets was based on data derived from the Japanese Nosocomial Infection Surveillance system. It is studied how the Java Neural Network Simulator learns to categorize these patients to predict their risk of nosocomial infection. The simulations are performed with several backpropagation learning algorithms and with several groups of parameters, comparing their results through the sum of the squared errors and mean errors per pattern.

Results

The backpropagation with momentum algorithm showed

SUBMITTER: Nistal-Nuno B 

PROVIDER: S-EPMC7664827 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature