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 out- comes 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 syn- dromes, 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.

Automated interviews on clinical case reports to elicit directed acyclic graphs / D. Luciani, F.M. Stefanini. - In: ARTIFICIAL INTELLIGENCE IN MEDICINE. - ISSN 0933-3657. - 55:1(2012), pp. 1-11.

Automated interviews on clinical case reports to elicit directed acyclic graphs

F.M. Stefanini
2012

Abstract

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 out- comes 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 syn- dromes, 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.
Knowledge acquisition; Bayesian networks; Directed acyclic graph; Diagnostic reasoning; Problem based learning
Settore SECS-S/01 - Statistica
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/849438
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