Use of data abstraction methods to simplify monitoring

I describe the Temporal Control System (TCS), a programming system designed for building intelligent temporal monitoring programs. The ICU data set provided as part of the 1994 AAAI Spring Symposium challenge is used to conduct several experiments. Empirical results from the ICU data set validate the scalable design of the TCS. The remaining experiments examine the computational problem of generating interval values from sample points through persistent assumptions. Using abstractions in combination with persistence assumptions makes the design of higher-level clinical reasoning programs simpler. Abstraction can be used to suppress clinically unimportant details, allowing an expert system to focus on the key information provided by clinical monitors. The TCS provides the framework for the implementation as well as a method of calculating the 'cost' of different approaches. To prevent the use of outdated information, it is often useful to limit the time span of a persistent interval. I show that such limitations can be very costly computationally and then show how the application of symbolic abstraction can help. Further performance improvements from switching from continuous to discrete step persistence are shown. These performance enhancing techniques have general applicability.