Computational modeling study demonstrates automated construction of decision trees and influence diagrams in HIV-associated pulmonary disease, suggesting improved real-time clinical decision support.
Clinical application of decision analysis has been limited by unfamiliarity of clinicians with the technique, large data requirements, and the length of time needed to construct models. In order to make decision modeling more accessible to clinicians, the authors developed a computer program to construct decision models automatically. The system contains two separate knowledge bases. One contains frames encoding knowledge of the medical domain, the evaluation of pulmonary disease in patients infected with the human immunodeficiency virus (HIV). The other contains rules of correct decision model construction that guide the selection of items from the domain knowledge base and their insertion into the decision model. The system can create either a tree or an influence diagram that satisfies previously published critiquing rules. The system has the potential to enable novices to construct useful decision models and to provide individualized decision-analytic advice to clinicians in real time.
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Sonnenberg et al. (1994) studied this question.
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