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This paper has two contributions: a) it proposes a web services-based infrastructure to support Clinical Decision Support Systems (CDSSs) for processing multi-domain medical data from the obstetrical, perinatal and neonatal care domains, and b) applies Software Performance Engineering (SPE) to the proposed infrastructure. This extends a XML-based framework(More)
A reengineered approach to the early prediction of preterm birth is presented as a complimentary technique to the current procedure of using costly and invasive clinical testing on high-risk maternal populations. Artificial neural networks (ANNs) are employed as a screening tool for preterm birth on a heterogeneous maternal population; risk estimations use(More)
BACKGROUND The skin temperature distribution of a healthy human body exhibits a contralateral symmetry. Some nociceptive and most neuropathic pain pathologies are associated with an alteration of the thermal distribution of the human body. Since the dissipation of heat through the skin occurs for the most part in the form of infrared radiation, infrared(More)
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(More)
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(More)
In earlier work, the research group successfully used artificial neural networks (ANNs) to estimate ventilation duration for adult intensive care unit (ICU) patients. The ANNs performed well in terms of correct classification rate (CCR) and average squared error (ASE) classifying the outcome into two classes: whether patients were ventilated for less(More)
The objective was to determine the optimal operating conditions for an artificial neural network (ANN) to estimate outcomes. The simulations involved using the 51 inputs while changing the desired output variable. Comparing the correct classification rate (CCR) of an ANN with that of a constant predictor (CP) results indicates the minimum number of sample(More)