An Intelligent Problem Solving Environment for Designing Explanation Models and for Diagnostic Reasoning in Probabilistic Domains
نویسندگان
چکیده
MEDICUS2 is an Intelligent Problem Solving Environment (IPSE) currently under development. It is designed to support i) the construction of explanation models, and ii) the training of diagnostic reasoning and hypotheses testing in domains of complex, fragile, and uncertain knowledge. MEDICUS is currently developed and applied in the epidemiological fields of environmentally caused diseases and human genetics. Uncertainty is handled by the Bayesian network approach. Thus the modelling task for the learner consists of creating a Bayesian network for the problem at hand. He / she may test hypotheses about the model, and the system provides help. This differs from existing reasoning systems based on Bayesian networks, i.e. in medical domains, which contain a built-in knowledge base that may be used but not created or modified by the learner. For supporting diagnostic reasoning, MEDICUS proposes diagnostic hypotheses and examinations. This will be extended to support learners' acquisition and training of diagnostic strategies.
منابع مشابه
Supporting the Construction of Explanation Models and Diagnostic Reasoning in Probabilistic Domains
MEDICUS (modeling, explanation, and diagnostic support for complex, uncertain subject matters) is an intelligent modeling and diagnosis environment designed to support the construction of explanation models and diagnostic reasoning in domains where knowledge is complex, fragile, and uncertain. MEDICUS is developed in collaboration with several medical institutions in the epidemiological fields ...
متن کاملA novel model of clinical reasoning: Cognitive zipper model
Introduction: Clinical reasoning is a vital aspect of physiciancompetence. It has been the subject of academic research fordecades, and various models of clinical reasoning have beenproposed. The aim of the present study was to develop a theoreticalmodel of clinical reasoning.Methods: To conduct our study, we applied the process of theorysynthesis in accordan...
متن کاملAcquiring Knowledge from Linguistic Models in Complex, Probabilistic Domains
This paper describes an approach to acquire qualitative and quantitative knowledge from verbally stated models in complex, probabilistic domains. This work is part of the development of an intelligent environment, MEDICUS2, that supports modelling and diagnostic reasoning in the domains of environmental medicine and human genetics. These domains are two yet new subdomains of medicine receiving ...
متن کاملSpecial Issue on Knowledge Representation and Machine Learning Five Useful Properties of Probabilistic Knowledge Representations from the Point of View of Intelligent Systems
Although probabilistic knowledge representations and probabilistic reasoning have by now secured their position in artiicial intelligence, it is not uncommon to encounter misunderstanding of their foundations and lack of appreciation for their strengths. This paper describes ve properties of probabilistic knowledge representations that are particularly useful in intelligent systems research. (1...
متن کاملAcquiring Qualitative and Quantitative Knowledge from Verbal Statements and Dialogues in Probabilistic Domains
We describe an approach to acquire qualitative and quantitative knowledge from verbal statements and dialogues in complex, probabilistic domains. This work is part of the development of an intelligent environment, MEDICUS (M odelling, e xplanation, and di agnostic support for c omplex, u ncertain s ubject matters), that supports modelling and diagnostic reasoning in the domains of environmental...
متن کامل