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Keith L. Downing

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AIIM Journal 1993 Journal Article

Physiological applications of consistency-based diagnosis

  • Keith L. Downing

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.

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AAAI Conference 1992 Conference Paper

Consistency-Based Diagnosis in Physiological Domains

  • Keith L. Downing

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. To combat this problem, we separate static from dynamic analysis, so that CBD is performed over the steady-state constraints at only a selected set of time slices. Regulatory models help link static inter-slice diagnoses into a complete dynamic account of the physiological progression. This provides a simpler approach to CBD in dynamic systems that (a) preserves information-reuse capabilities, (b) extends information-theoretic probing, and (c) adds a new capability to CBD: the detection of dynamic faults (i. e. , those that do not necessarily persist throughout diagnosis).

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AAAI Conference 1987 Conference Paper

Diagnostic Improvement through Qualitative Sensitivity Analysis and Aggregation

  • Keith L. Downing

This paper lays the foundation for a diagnostic system that improves its performance by deriving symptom-fault associations from an underlying causal model and then utilizes those relationships to impose further structure upon the "deep" model. A qualitative version of sensitivity analysis is introduced to extract the implicit symptom-fault information from a set of local constraints. Parameter aggregation triggered by this new information then simplifies diagnosis by forming a more abstract causal representation. The resulting diagnostician thus employs both an experiential and a first-principle approach, where in this case "experiences" are compiled directly from first-principles. Key issues include the roles of knowledge compilation and abstraction in refining qualitative models of physical systems.

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