Christina Catley

Design of a health care architecture for medical data interoperability and application integration

The Internet is changing the face of medical research. The current world of isolated research and proprietary data encodings is evolving into a future of standardized medical databases and integrated medical applications, such as clinical decision support systems. This paper explores the use of XML, and its associated Schema Language, to enhance sharing of medical data. XML enables data portability and will reach its full potential when the medical community develops a standardized basis for medical schema content and shares these schemas in recognized repositories. Our research group is currently harnessing XMLs standardization potential by designing a standards-compliant, medical information infrastructure that will allow for seamless integration of all our clinical decision support tools.

Predicting High-Risk Preterm Birth Using Artificial Neural Networks

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 obstetrical variables available to physicians before 23 weeks gestation. The objective was to assess if ANNs have a potential use in obstetrical outcome estimations in low-risk maternal populations. The back-propagation feedforward ANN was trained and tested on cases with eight input variables describing the patients obstetrical history; the output variables were: 1) preterm birth; 2) high-risk preterm birth; and 3) a refined high-risk preterm birth outcome excluding all cases where resuscitation was delivered in the form of free flow oxygen. Artificial training sets were created to increase the distribution of the underrepresented class to 20%. Training on the refined high-risk preterm birth model increased the networks sensitivity to 54.8%, compared to just over 20% for the nonartificially distributed preterm birth model

Integrating clinical alerts into an XML-based health care framework for the neonatal intensive care unit

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 physicians 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.

Towards Ethical Decision Support and Knowledge Management in Neonatal Intensive Care

Recent studies in neonatal medicine, clinical nursing, and cognitive psychology have indicated the need to augment current decision-making practice in neonatal intensive care units with computerized, intelligent decision support systems. Rapid progress in artificial intelligence and knowledge management facilitates the design of collaborative ethical decision-support tools that allow clinicians to provide better support for parents facing inherently difficult choices, such as when to withdraw aggressive treatment. The appropriateness of using computers to support ethical decision-making is critically analyzed through research and literature review. In ethical dilemmas, multiple diverse participants need to communicate and function as a team to select the best treatment plan. In order to do this, physicians require reliable estimations of prognosis, while parents need a highly useable tool to help them assimilate complex medical issues and address their own value system. Our goal is to improve and structuralize the ethical decision-making that has become an inevitable part of modern neonatal care units. The paper contributes to clinical decision support by outlining the needs and basis for ethical decision support and justifying the proposed development efforts.

Healthcare Knowledge Management: Knowledge Management in the Perinatal Care Environment

The chapter presents four key steps in the knowledge management process: access to quality clinical data; knowledge discovery; knowledge translation; and knowledge integration and sharing. Examples are provided for each of these steps for the perinatal care clinical environment and a number of artificial intelligence tools and analyses results are described. The usefulness of this approach for clinical decision support is discussed and the chapter concludes with suggestions on knowledge integration and sharing using Web services.

Predicting preterm birth using artificial neural networks

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 ANNs 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 patients 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.

Student leadership: biomedical engineering initiatives at Carleton University

This paper presents biomedical engineering education initiatives, from a student perspective, at Carleton University in Ottawa, Canada. Over recent years, efforts made by graduate students and professors have been noticeably successful in promoting biomedical engineering to students as young as 13. A recent biomedical engineering career event drew considerable positive attention and precipitated the consolidation of an already strong emerging biomedical core within the department of Systems and Computer Engineering. Capitalizing on the success of past events, an Engineering in Medicine and Biology Society (EMBS) student club was created with the express purpose of gathering all interesting parties within the same framework. The clubs main goal is to develop a strong biomedical education program in order to attract potential students, and create strong partnerships with the biomedical industry.

Using Artificial Intelligence to Estimate Outcomes in Perinatal Medicine

A combination of tools that include artificial neural networks (ANNs) and case-based reasoning (CBR) allow the development of prediction models that have the potential to help physicians in their tasks of making a diagnosis and deciding on a course of therapy. The models developed by our research group to date predict the occurrence of pre-term births, delivery type, and Apgar score. Future work will include testing the prototypes in a clinical setting. Such systems have the potential to add value to current clinical tools and improve predictions and the management of patients for better clinical outcomes