Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
AIME '01 Proceedings of the 8th Conference on AI in Medicine in Europe: Artificial Intelligence Medicine
Sensitivity analysis: an aid for belief-network quantification
The Knowledge Engineering Review
Clinical-reasoning skill acquisition through intelligent group tutoring
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
How to elicit many probabilities
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Graphical models for preference and utility
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Elicitation of probabilities for belief networks: combining qualitative and quantitative information
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
A method for evaluating elicitation schemes for probabilisticmodels
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Editorial: Bayesian networks in biomedicine and health-care
Artificial Intelligence in Medicine
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Objective: Setting up clinical reports within hospital information systems makes it possible to record a variety of clinical presentations. Directed acyclic graphs (Dags) offer a useful way of representing causal relations in clinical problem domains and are at the core of many probabilistic models described in the medical literature, like Bayesian networks. However, medical practitioners are not usually trained to elicit Dag features. Part of the difficulty lies in the application of the concept of direct causality before selecting all the causal variables of interest for a specific patient. We designed an automated interview to tutor medical doctors in the development of Dags to represent their understanding of clinical reports. Methods: Medical notions were analyzed to find patterns in medical reasoning that can be followed by algorithms supporting the elicitation of causal Dags. Clinical relevance was defined to help formulate only relevant questions by driving an expert's attention towards variables causally related to nodes already inserted in the graph. Key procedural features of the proposed interview are described by four algorithms. Results: The automated interview comprises questions on medical notions, phrased in medical terms. The first elicitation session produces questions concerning the patient's chief complaints and the outcomes related to diseases serving as diagnostic hypotheses, their observable manifestations and risk factors. The second session focuses on questions that refine the initial causal paths by considering syndromes, dysfunctions, pathogenic anomalies, biases and effect modifiers. A case study concerning a gastro-enterological problem and one dealing with an infected patient illustrate the output produced by the algorithms, depending on the answers provided by the doctor. Conclusions: The proposed elicitation framework is characterized by strong consistency with medical background and by a progressive introduction of relevant medical topics. Revision and testing of the subjectively elicited Dag is performed by matching the collected answers with the evidence included in accepted sources of biomedical knowledge.