Information retrieval and the structure of the biomedical lexicon
ACM SIGBIO Newsletter
MDS: An Integrated Architecture for Associational and Model-Based Diagnosis
Applied Intelligence
Using Compiled Knowledge to Guide and Focus Abductive Diagnosis
IEEE Transactions on Knowledge and Data Engineering
Symbolic diagnosis and its formalisation
The Knowledge Engineering Review
Checking the quality of clinical guidelines using automated reasoning tools
Theory and Practice of Logic Programming
A theory of diagnosis for incomplete causal models
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Model-based diagnosis in the real world: lessons learned and challenges remaining
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
On using causal knowledge to recognize vital signals: knowledge-based interpretation of arrhythmias
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Modelling diagnostic skills in the domain of skeletal dysplasias
Computer Methods and Programs in Biomedicine
A simulation-based tutor that reasons about multiple agents
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Guardian: A prototype intelligent agent for intensive-care monitoring
Artificial Intelligence in Medicine
On the co-operation between abductive and temporal reasoning in medical diagnosis
Artificial Intelligence in Medicine
Multiple representations and multi-modal reasoning in medical diagnostic systems
Artificial Intelligence in Medicine
Medical informatics: reasoning methods
Artificial Intelligence in Medicine
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Much of the medical knowledge in the first generation Al in Medicine programs is phenomenological; that is, it describes the associations among phenomena without knowledge of the underlying causal mechanisms. Although these AIM programs provide a good first approximation of the way clinicians reason, they fail to reproduce clinicians'' reasoning based on a deeper understanding of the phenomena. More specifically, they do not deal with the knowledge of disease at different levels of detail, nor do they utilize causal relations to organize and explain the clinical facts and disease hypothesis. They also cannot deal with illnesses resulting from multiple diseases, especially when one disease alters the presentation of the others. Finally, they are unable to capture the notions of adequacy and parsimony that play such a large role in diagnosis. To explore these issues and rectify these deficiencies, we have undertaken the task of providing expert consultation for electrolyte and acid-base disturbances. This thesis reports the implementation of ABEL, the diagnostic component of the consultation program. In it, we explore the problems of modeling the causal understanding of a patient''s illness. We develop techniques for dealing with illness resulting from multiple interacting diseases. We describe a multi-level representation of causal knowledge, and explore issues of the aggregation of available case specific knowledge into concise summaries of the patient''s illness. We discuss structural criteria for evaluation parsimony, coherence and adequacy of diagnostic explanations. We also explore some of the issues involved in information gathering and propose expectation-driven diagnostic planning as a means of improving it. Finally, we discuss the issues of explanation and justification of the program''s understanding and argue that these facilities are crucial for acceptability of a consultation program.