Biomedical subjects

C M Ennett

Publications and source records attributed to C M Ennett.

4 recordsLinked to original sources

PubMed2001

Clinical decision support systems for intensive care units: using artificial neural networks.

The paper provides an overview of applications of artificial neural networks (ANNs) to various medical problems, with a particular focus on the intensive care unit environment (ICU). Several technical approaches were tested to see whether they improve the ANN performance in estimating medical outcomes and resource utilization in adult ICUs. These experiments include: (1) use of the weight-elimination cost function; (2) use of 'high' and 'low' nodes for input variables; (3) verifying the effect of the total number of input variables on the results; (4) testing the impact of the value of the constant predictor on the performance of the ANNs. The developments presented intend to help medical and nursing personnel to assess patient status, assist in making a diagnosis, and facilitate the selection of a course of therapy.

APACHE↗

PubMed2001

Influence of missing values on artificial neural network performance.

The problem of databases containing missing values is a common one in the medical environment. Researchers must find a way to incorporate the incomplete data into the data set to use those cases in their experiments. Artificial neural networks (ANNs) cannot interpret missing values, and when a database is highly skewed, ANNs have difficulty identifying the factors leading to a rare outcome. This study investigates the impact on ANN performance when predicting neonatal mortality of increasing the number of cases with missing values in the data sets. Although previous work using the Canadian Neonatal Intensive Care Unit (NICU) Network s database showed that the ANN could not correctly classify any patients who died when the missing values were replaced with normal or mean values, this problem did not arise as expected in this study. Instead, the ANN consistently performed better than the constant predictor (which classifies all cases as belonging to the outcome with the highest training set a priori probability) with a 0.6-1.3% improvement over the constant predictor. The sensitivity of the models ranged from 14.5-20.3% and the specificity ranged from 99.2- 99.7%. These results indicate that nearly 1 in 5 babies who will eventually die are correctly classified by the ANN, and very few babies were incorrectly identified as patients who will die. These findings are important for patient care, counselling of parents and resource allocation.

Decision Support Systems, Clinical↗

PubMed2000

Selective sampling to overcome skewed a priori probabilities with neural networks.

Highly skewed a priori probabilities present challenges for researchers developing medical decision aids due to a lack of information on the rare outcome of interest. This paper attempts to overcome this obstacle by artificially increasing the mortality rate of the training sets. A weight pruning technique called weight-elimination is also applied to this coronary artery bypass grafting (CABG) database to assess its impact on the artificial neural network's (ANN) performance. The results showed that increasing the mortality rate improved the sensitivity rates at the cost of the other performance measures, and the weight-elimination cost function improved the sensitivity rate without seriously affecting the other performance measures.

Algorithms↗

PubMed1998

New advances and validation of knowledge management tools for critical care using classifier techniques.

An earlier version (2.0) of the case-based reasoning (CBR) tool, called IDEAS for ICU's, allowed users to compare the ten closest matching cases to the newest patient admission, using a large database of intensive care patient records, and physician-selected matching-weights [1,2]. The new version incorporates matching-weights, which have been determined quantitatively. A faster CBR matching engine has also been incorporated into the new CBR. In a second approach, a back-propagation, feed-forward artificial neural network estimated two classes of the outcome "duration of artificial ventilation" for a subset of the database used for the CBR work. Weight-elimination was successfully applied to reduce the number of input variables and speed-up the estimation of outcomes. New experiments examined the impact of using a different number of input variables on the performance of the ANN, measured by correct classification rates (CCR) and the Average Squared Error (ASE).

Critical Care↗