Artificial Intelligence
A theory of diagnosis from first principles
Artificial Intelligence
Artificial Intelligence
Artificial intelligence techniques for diagnostic reasoning in medicine
Exploring artificial intelligence
Semi-quantitative “close enough” systems dynamics models: an alternative to qualitative simulation
Artificial intelligence, simulation & modeling
Using crude probability estimates to guide diagnosis
Artificial Intelligence
Modeling digital circuits for troubleshooting
Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
What's in SD?: Towards a theory of modeling for diagnosis
Readings in model-based diagnosis
Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence)
Diagnosis with behavioral modes
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Diagnostic improvement through qualitative sensitivity analysis and aggregation
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 2
Dynamic across-time measurement interpretation
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Editorial: Intelligent monitoring and control of dynamic physiological systems
Artificial Intelligence in Medicine
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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.