Sim•TwentyFive: An Interactive Visualization System for PICU Decision Support
نویسنده
چکیده
Pediatric intensive care unit (PICU) physicians need better decision support tools for finding and exploring similar patients to a given patient-of-interest. Recently, unsupervised machine learning methods have been shown to be effective at generating similarity scores between patients based on temporal physiological data. In this paper, we present Sim•TwentyFive, an interactive visualization decision support tool for the exploration of physiologically-similar patients that is based on these novel computational methods. Our system is highly flexible, responsive, intuitive and effectively reduces the cognitive burden of querying and investigating similar patients.
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تاریخ انتشار 2011