Journal of biomedical informatics
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Predicting the survival status of Intensive Care patients at the end of their hospital stay is useful for various clinical and organizational tasks. Current models for predicting mortality use logistic regression models that rely solely on data collected during the first 24h of patient admission. These models do not exploit information contained in daily organ failure scores which nowadays are being routinely collected in many Intensive Care Units. ⋯ We compared our models with ones that were developed on the same patient subpopulations but which did not use the episodes. The new models show improved performance on each of the five days. They also provide insight in the effect of the various selected episodes on mortality.
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Review Meta Analysis
Conceptual knowledge acquisition in biomedicine: A methodological review.
The use of conceptual knowledge collections or structures within the biomedical domain is pervasive, spanning a variety of applications including controlled terminologies, semantic networks, ontologies, and database schemas. A number of theoretical constructs and practical methods or techniques support the development and evaluation of conceptual knowledge collections. This review will provide an overview of the current state of knowledge concerning conceptual knowledge acquisition, drawing from multiple contributing academic disciplines such as biomedicine, computer science, cognitive science, education, linguistics, semiotics, and psychology. In addition, multiple taxonomic approaches to the description and selection of conceptual knowledge acquisition and evaluation techniques will be proposed in order to partially address the apparent fragmentation of the current literature concerning this domain.
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The intensive care unit (ICU) is an instance of a very dynamic health care setting where critically ill patients are being managed. To provide good care, an extensive and coordinated communication amongst the role players, use of numerous information systems and operation of devices for monitoring and treatment purposes are required. The purpose of this research is to study error evolution and management within this environment. ⋯ These clinicians and nurses were interviewed to complement the observation data and to delineate their individual workflows. These pieces of the ICU workflow were used to develop a generalize-able cognitive model to represent the intricate workflow applicable to other health care settings. The proposed model can be used to identify and characterize medical errors and for error prediction in practice.
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Using a local percutaneous coronary intervention (PCI) data repository, we sought to compare the performance of a number of local and well-known mortality models with respect to discrimination and calibration. ⋯ Validation of AUC values across all models suggests that certain risk factors have remained important over the last decade. However, the lack of calibration suggests that small changes in patient populations and data collection methods quickly reduce the accuracy of patient level estimations over time. Possible solutions to this problem involve either recalibration of models using local data or development of new local models.