A theory of diagnosis from first principles
Artificial Intelligence
Introduction to algorithms
Abductive inference models for diagnostic problem-solving
Abductive inference models for diagnostic problem-solving
Abduction versus closure in causal theories
Artificial Intelligence
Temporal constraint satisfaction on causal models
Information Sciences: an International Journal
A model and a language for the fuzzy representation and handling of time
Fuzzy Sets and Systems
Fuzzy relation equations and causal reasoning
Fuzzy Sets and Systems - Special issue: fuzzy relations, part 2
Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence)
Temporal scenario modelling and recognition based on possibilistic logic
Artificial Intelligence - Special issue: Fuzzy set and possibility theory-based methods in artificial intelligence
Computer aided fuzzy medical diagnosis
Information Sciences: an International Journal - Special issue: Medical expert systems
Temporal reasoning for decision support in medicine
Artificial Intelligence in Medicine
Fuzzy theory approach for temporal model-based diagnosis: An application to medical domains
Artificial Intelligence in Medicine
Journal of Biomedical Informatics
A framework for application of neuro-case-rule base hybridization in medical diagnosis
Applied Soft Computing
Discriminating exanthematic diseases from temporal patterns of patient symptoms
AIME'05 Proceedings of the 10th conference on Artificial Intelligence in Medicine
A web based decision support system driven by fuzzy logic for the diagnosis of typhoid fever
Expert Systems with Applications: An International Journal
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This paper proposes a way of incorporating fuzzy temporal reasoningwithin diagnostic reasoning. Disorders are described as an evolving setof necessary and possible manifestations. Ill-known moments in time,e.g., when a manifestation should start or end, are modeled by fuzzyintervals, which are also used to model the elapsed time betweenevents, e.g., the beginning of a manifestation and its end. Patientinformation about the intensity and times in which manifestationsstarted and ended are also modeled using fuzzy sets. The paperdiscusses many measures of consistency between the patient‘s data andthe disorder model, and defines when the manifestations of the patientcan be explained by a disorder. This work also discusses related issuessuch as the intensity of manifestations and the speed in which thedisorder is evolving, given the patient‘s data, and how to use thatinformation to make predictions about future and past events.