This paper discusses the results of applying artificial neural networks to predicting complication for neonatal intensive care patients. Risk factors that lead to necrotizing entero-colitis or broncho-pulmonary dysplasia were identified. Future work will expand this work to other outcomes and add probability information to the estimations.

This research is built on the belief that artificial intelligence estimations need to be integrated into clinical social context to create value for health-care decisions. In sophisticated neonatal intensive care units (NICUs), decisions to continue or discontinue aggressive treatment are an integral part of clinical practice. High-quality evidence supports clinical decision-making, and a decision-aid tool based on specific outcome information for individual NICU patients will provide significant support for parents and caregivers in making difficult "ethical" treatment decisions. In our approach, information on a newborn patient's likely outcomes is integrated with the physician's interpretation and parents' perspectives into codified knowledge. Context-sensitive content adaptation delivers personalized and customized information to a variety of users, from physicians to parents. The system provides structuralized knowledge translation and exchange between all participants in the decision, facilitating collaborative decision-making that involves parents at every stage on whether to initiate, continue, limit, or terminate intensive care for their infant.

This paper has three contributions: 1) to evaluate how changing the a priori distribution of the training set affects the performance of a back-propagation feed-forward artificial neural network (ANN) in predicting PreTerm Birth (PTB) for obstetrical patients, 2) to assess the effectiveness of the weight elimination cost function in improving the ANN's classification of PTB and in identifying a new minimal dataset, and (3) to determine if PTB can be predicted outside of clinical trial situations using data readily available to the physician during obstetrical care. The ANN was trained and tested on cases with 8 input variables describing the patient's obstetrical history; the output variable was PTB before 37 weeks gestation. To observe the impact of training with a higher-than-normal prevalence, an artificial training set with a PTB rate of 23% was created. Networks trained on higher-than-normal prevalence achieved higher sensitivity rates and greater C-index values, at the cost of slightly lower specificity and correct classification rates.

Two different approaches, based on artificial neural networks (ANN) and fuzzy logic, were used to predict a number of outcomes of newborns: How they would be delivered, their 5 minute Apgar score, and neonatal mortality. The goal was to assess whether the methods would be comparable or whether they would perform differently for different outcomes. The results were comparable for Correct Classification Rate (CCR) and Specificity (true negative cases). Sensitivity (true positive cases) was slightly higher for the back-propagation feed-forward ANN than using the Fuzzy-Logic Classifier (FLC). Since this is one single database and a very large one, it is possible that the FLC would perform better than the ANN for very small databases, as shown by some of the co-authors in the past. The next step will be to test a small database with both methods to assess strengths and weaknesses with the intent to use both if needed with some medical data in the future.

This paper presents the design of a unifying infrastructure for clinical decision support systems (CDSSs) and medical data relating to the perinatal life cycle. The diverse CDSSs designed for deployment within the perinatal life cycle to improve care, such as Artificial Neural Networks and Case-Based Reasoners, are integrated using the eXtended Markup Language (XML) and are subsequently offered as a secure web service. These web services are accessible from anywhere within the hospital information system and from remote authorized sites. The goal of such an infrastructure is to provide integrated CDSS processing in a complex distributed environment, in order to support real-time physician decision-making. This design provides a novel web services infrastructure implementation and offers a strong case study for deploying and evaluating the web services paradigm within a health care environment.

Biomedical Engineering is a multidisciplinary field that offers opportunities for physicians and other health care personnel to work with engineers. Examples of this type of collaboration and the types of problems that can be solved are discussed. Three main areas of research-are presented: Clinical Decision-support Systems, Medical Imaging, and Web Services for Health Care Applications.

This work extends the functionality of our earlier XML-based health care framework for integrating clinical decision support systems (CDSSs) with capabilities for defining, detecting, and generating clinical alerts in the neonatal intensive care unit (NICU). A first step in this work involved creating a complete NICU XML schema for defining and constraining medical device data, CDSS inputs and outputs, and clinical alerts. The alerts are customizable through a flexible user interface that automatically creates XML documents based on the physician's input specifications. XML documents are transmitted to a central Java application for alert display and transmission. Transmitting XML-based alerts allows the alert information to be shareable in many contexts- within and between hospital information systems, and from remote locations. This is particularly useful when one considers the possibility of offering CDSS-generated alerting systems as ubiquitous web services to pre-authorized users.

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.