User-centered visual analysis using a hybrid reasoning architecture for intensive care units

One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care

On the differences between bubble-mediated air-water transfer in freshwater and seawater

Bubble populations and gas transfer velocities were measured in cleaned and surfactant-influenced freshwater and seawater. A nonlinear fitting technique was used to partition the total gas transfer velocity for a gas in each water type into a turbulence- and bubble-mediated fraction. This showed that the bubble-mediated transfer fraction was larger in cleaned freshwater than in cleaned seawater and that the difference was a function of diffusivity and solubility. This was explained by the fact that the bubble measurements showed that bubble plumes in cleaned freshwater had a higher concentration of large bubbles and a lower concentration of small bubbles than the plumes in cleaned seawater. The differences between the behavior of the bubble-mediated gas flux in cleaned freshwater and cleaned seawater show that caution should be used when intercomparing laboratory results from measurements made in different media. These differences also will make parameterizations of bubble-mediated gas exchange developed using freshwater laboratory data difficult to apply directly to oceanic conditions. It was found that adding a surfactant to seawater had minimal impact on the concentration of bubbles in the plumes. Because surfactants decrease the gas flux to the individual bubbles, the similarity in bubble population meant that the addition of surfactant to seawater decreased the bubble-mediated gas flux compared to the flux in cleaned seawater. In contrast, the addition of a surfactant to freshwater increased the concentration of bubbles by over an order of magnitude. This increase in bubble population was large enough to offset the decrease in the flux to the individual bubbles so that the net bubble-mediated gas flux in freshwater increased when surfactant was added. This difference in behavior of the bubble population and bubble-mediated transfer velocity between surfactant-influenced and cleaned waters further complicates interrelating laboratory measurements and applying laboratory results to the ocean

Endorsement, Prior Action, and Language: Modeling Trusted Advice in Computerized Clinical Alerts

The safe prescribing of medications via computerized physician order entry routinely relies on clinical alerts. Alert compliance, however, remains surprisingly low, with up to 95% often ignored. Prior approaches, such as improving presentational factors in alert design, had limited success, mainly due to physicians' lack of trust in computerized advice. While designing trustworthy alert is key, actionable design principles to embody elements of trust in alerts remain little explored. To mitigate this gap, we introduce a model to guide the design of trust-based clinical alerts-based on what physicians value when trusting advice from peers in clinical activities. We discuss three key dimensions to craft trusted alerts: using colleagues' endorsement, foregrounding physicians' prior actions, and adopting a suitable language. We exemplify our approach with emerging alert designs from our ongoing research with physicians and contribute to the current debate on how to design effective alerts to improve patient safety