We describe our experience building and using a reasoning system for providing context-based prompts to elders to take their medication. We describe the process of specification, design, implementation and use of our system. We chose a simple Dynamic Bayesian Network as our representation. We analyze the design space for the model in some detail. A key challenge in using the model was the overhead of labeling the data. We analyze the impact of a variety of options to ease labeling, and highlight in particular the utility of simple clustering before labeling. A key choice in the design of such reasoning systems is that between statistical and deterministic rule-based approaches. We evaluate a simple rule-based system on our data and discuss some of its pros and cons when compared to the statistical (Bayesian) approach in a practical setting. We discuss challenges to reasoning arising from failures of data collection procedures and calibration drift. The system was deployed among 6 subjects over a period of 12 weeks, and resulted in adherence improving from 56% on average with no prompting to 63% with state of the art context-unaware prompts to 74% with our context-aware prompts. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Vurgun, S., Philipose, M., & Pavel, M. (2007). A statistical reasoning system for medication prompting. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4717 LNCS, pp. 1–18). Springer Verlag. https://doi.org/10.1007/978-3-540-74853-3_1
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