Dynamic network construction and updating techniques for the diagnosis of acute abdominal pain
Gregory M. Provan, John R. Clarke · IEEE Transactions on Pattern Analysis and Machine Intelligence · 1993
Computing diagnoses in domains with continuously changing data is difficult but essential aspect of solving many problems. To address this task, a dynamic influence diagram (ID) construction and updating system (DYNASTY) and its application to constructing a decision-theoretic model to diagnose acute abdominal pain, which is a domain in which the findings evolve during the diagnostic process, are described. For a system that evolves over time, DYNASTY constructs a parsimonious ID and then dynamically updates the ID, rather than constructing a new network from scratch for every time interval. In addition, DYNASTY contains algorithms that test the sensitivity of the constructed network's system parameters. The main contributions are: (1) presenting an efficient temporal influence diagram technique based on parsimonious model construction; and (2) formalizing the principles underlying a diagnostic tool for acute abdominal pain that explicitly models time-varying findings.>