Physiological applications of consistency-based diagnosis.
This research attempts to span the gap between the AI in medicine (AIM) and consistency-based diagnosis (CBD) communities by applying CBD to physiology. The highly-regulated nature of physiological systems challenges standard CBD algorithms, which are not tailored for complex dynamic systems. Extensions of CBD to dynamic domains have relied upon complete quantitative dynamic simulation for behavior prediction. However, dynamic simulations, particularly by continuous systems, tend to inundate key CBD processes (such as truth maintenance and information-theoretic testing) with a deluge of temporal information. To combat this problem, we separate static from dynamic analysis so that CBD performs static diagnosis at a selected set of time slices. Knowledge of the qualitative behavior of physiological regulators is then used to link static intra-slice diagnoses into a complete dynamic account of the progression of a physiological condition. This provides a simpler approach to CBD of dynamic systems while adding a new capability to CBD: the detection of dynamic faults (i.e. those that do not necessarily persist throughout diagnosis). This paper describes (a) a few of the problems underlying CBD extensions to dynamic systems, (b) our hybrid static-dynamic, qualitative-quantitative approach, (c) our implemented IDUN system, (d) IDUN's diagnosis of volume-loading hypertension, (e) the generalization of IDUN's modeling perspective to the compartmental ontology, and (f) IDUN's use of compartmental models to diagnose acidosis.
Downing
K L
KL
Department of Computer and Information Science, Linköping University, Sweden.
D000138:Acidosis / Q000175:diagnosis / Q000503:physiopathology; D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted; D006801:Humans; D006973:Hypertension / Q000175:diagnosis / Q000503:physiopathology; D007257:Information Theory; D008954:Models, Biological; D010827:Physiology / Q000295:instrumentation*