Intelligent diagnostic monitoring using trend templates

In previous work we have defined our trend template epistemology for clinically significant trends and we have illustrated and tested a program TrenDx that monitors time-ordered process data by matching the data to trend templates. Our initial application domain was pediatric growth monitoring. In continuing work we have explored monitoring hemodynamic and respiratory parameters of intensive care unit patients. This application has highlighted the needs for advances in our representation and monitoring algorithms. In particular, we have added reasoning with uncertainty to the trend template epistemology, and a new control structure allowing numerical ranking of competing trend templates. Furthermore, intelligent monitoring in any medical domain requires a coherent framework for diagnostic monitoring. In this paper we show how TrenDx can be extended to a framework including sending alarms, changing clinical context, and filtering data streams.