Bayesian networks for cardiovascular monitoring

Technology: A metaparadigm concept of nursing

Undoubtedly, technology continues to permeate the world at an unprecedented pace. The discipline of nursing is not alien to this phenomenon as nurses continue to employ various technological objects and applications in clinical practice, education, administration and research. Despite the centrality of technology in nursing, it has not been recognised as a metaparadigm domain of interest in the discipline of nursing. Thus, this paper sought to examine if technology truly reflected a metaparadigm domain using the four requirements posited by Fawcett. Using these requirements, we examined the onto-epistemology of technology in relation to nursing and conclude that technology potentially represents a distinct domain that intersects with nursing (particularly, from the humanities perspective). Also, technology encompasses some phenomena of interest to the discipline of nursing, demonstrates perspective-neutrality, and is international in scope and substance albeit with some nuances which do not fit well with nursing onto-epistemology. Put together, it is highlighted that technology intersects with the existing metaparadigm domains (person, health, environment and nursing) which positions it as a potential phenomenon of interest to the discipline of nursing requiring further work to articulate its position and role.

Robust parameter extraction for decision support using multimodal intensive care data

Digital information flow within the intensive care unit (ICU) continues to grow, with advances in technology and computational biology. Recent developments in the integration and archiving of these data have resulted in new opportunities for data analysis and clinical feedback. New problems associated with ICU databases have also arisen. ICU data are high-dimensional, often sparse, asynchronous and irregularly sampled, as well as being non-stationary, noisy and subject to frequent exogenous perturbations by clinical staff. Relationships between different physiological parameters are usually nonlinear (except within restricted ranges), and the equipment used to measure the observables is often inherently error-prone and biased. The prior probabilities associated with an individual's genetics, pre-existing conditions, lifestyle and ongoing medical treatment all affect prediction and classification accuracy. In this paper, we describe some of the key problems and associated methods that hold promise for robust parameter extraction and data fusion for use in clinical decision support in the ICU.

Integrating data, models, and reasoning in critical care

Modern intensive care units (ICUs) employ an impressive array of technologically sophisticated instrumentation to provide detailed measurements of the pathophysiological state of each patient. Providing life support in the ICU is becoming an increasingly complex task, however, because of the growing volume of relevant data from clinical observations, bedside monitors, mechanical ventilators, and a wide variety of laboratory tests and imaging studies. The enormous amount of ICU data and its poor organization makes its integration and interpretation time-consuming and inefficient. There is a critical need to integrate the disparate clinical information into a single, rational framework and to provide the clinician with hypothesis-driven displays that succinctly summarize a patient's trajectory over time. In this paper, we present our recent efforts towards the development of such an advanced patient monitoring system that aims to improve the efficiency, accuracy, and timeliness of clinical decision making in intensive care.