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Biomedical subjects

B Parmanto

Publications and source records attributed to B Parmanto.

5 recordsLinked to original sources
PubMed2001

Detection of hemodynamic changes in clinical monitoring by time-delay neural networks.

Small changes that occur in a patient's physiology over long periods of time are difficult to detect, yet they can lead to catastrophic outcomes. Detecting such changes is even more difficult in intensive care unit (ICU) environments where clinicians are bombarded by a barrage of complex monitoring signals from various devices. Early detection accompanied by appropriate intervention can lead to improvement in patient care. Neural networks can be used as the basis for an intelligent early warning system. We developed time-delay neural networks (TDNN) for classifying and detecting hemodynamic changes. A matrix of physiological parameters were extracted from raw signals collected during cardiovascular experiments in mongrel dogs. These matrices represented several episodes of stable, decreasing, and increasing cardiac filling in normal, exerted, and heart failure conditions. The TDNN were trained with these matrices and subsequently tested to predict unseen cases. The TDNN perform remarkably not only in identifying all hemodynamic conditions, but also in quickly detecting their changes. On average, the networks were able to detect the hemodynamic changes in less than 1 s after the onset. Based on the results of this pilot investigation, the use of this form of TDNN to successfully predict hemodynamic conditions appears to be promising.

Animals↗
PubMed2001

Recurrent neural networks for predicting outcomes after liver transplantation: representing temporal sequence of clinical observations.

OBJECTIVES: This paper investigates a version of recurrent neural network with the backpropagation through time (BPTT) algorithm for predicting liver transplant graft failure based on a time series sequence of clinical observations. The objective is to improve upon the current approaches to liver transplant outcome prediction by developing a more complete model that takes into account not only the preoperative risk assessment, but also the early postoperative history. METHODS: A 6-fold cross-validation procedure was used to measure the performance of the networks. The data set was divided into a learning set and a test set by maintaining the same proportion of positive and negative cases in the original set. The effects of network complexity on overfitting were investigated by constructing two types of networks with different numbers of hidden units. For each type of network, 10 individual networks were trained on the learning set and used to form a committee. The performance of the networks was measured exhaustively with respect to both the entire training and test sets. RESULTS: The networks were capable of learning the time series problem and achieved good performances of 90% correct classification on the learning set and 78% on the test set. The prediction accuracy increases as more information becomes progressively available after the operation with the daily improvement of 10% on the learning set and 5% on the test set. CONCLUSIONS: Recurrent neural networks trained with BPTT algorithm are capable of learning to represent temporal behavior of the time series prediction task. This model is an improvement upon the current model that does not take into account postoperative temporal information.

Adult↗
PubMed1995

Building clinical classifiers using incomplete observations--a neural network ensemble for hepatoma detection in patients with cirrhosis.

One objective of liver transplant evaluation is to identify patients that harbor a hepatoma, but standard screening techniques are not sensitive enough. We trained neural network ensembles to predict the presence of hepatoma in patients with cirrhosis, based on information collected at the time of transplant evaluation. Network architecture and training were modified to handle missing observations. Three ensembles were trained: ensemble A using the subset with no missing observations (528 patients); ensemble B using the complete set, which included missing observations (853 patients); and ensemble C using the smaller subset, originally with complete data, but after a fixed number of observations were deleted (i.e., made "missing"). Ensemble performance on testing sets was very good. The areas under the ROC curves were 0.91, 0.89, and 0.90, for ensembles A, B, and C, respectively. Neural networks can successfully perform this classification task, and strategies can be developed that allow use of incomplete observations.

Adult↗