Publications

We present an educational tool for bringing the information contained in a Bayesian network to the end user in an easily intelligible form. The banter shell is designed to tutor users in evaluation of hypotheses and selection of optimal diagnostic procedures. banter can be used with any Bayesian network containing nodes that can be classified into hypotheses, observations, and diagnostic procedures. The system enables one to present various types of queries to the network, to test one's ablity to select optimal diagnostic procedures, and to request explanations. We describe the system's capabil­ ities by illustrating how it functions with two structurally different network models of real­world medical problems.

Decision­-theoretic refinement planning is a new technique for finding optimal courses of ac­ tion. The authors sought to determine whether this technique could identify optimal strate­ gies for medical diagnosis and therapy. An existing model of acute deep venous thrombosis of the lower extremities was encoded for analysis by the drips decision­theoretic refinement planning system. The encoding represented 6,206 possible plans. The drips planner used Artificial Intelligence techniques to eliminate 5,150 plans (83%) from consideration without examining them explicitly. drips identified the five strategies that minimized cost and mor­ tality. We conclude that decision­theoretic planning is useful for examining large medical decision problems.

Clinical decision analysis seeks to identify the optimal management strategy by modelling the un­ certainty and risks entailed in the diagnosis, natu­ ral history, and treatment of a particular problem or disorder. Decision trees are the most frequently used model in clinical decision analysis, but can be tedious to construct, cumbersome to use, and computationally prohibitive, especially with large, complex decision problems. We present a new method for clinical decision analysis that combines the techniques of decision theory and artificial in­ telligence. Our model uses a modular represen­ tation of knowledge that simplifies model building and enables more fully automated decision mak­ ing. Moreover, the model exploits problem struc­ tures to yield better computational efficiency. As an example we apply our techniques to the problem of management of acute deep venous thrombosis.

Bayesian networks use the techniques of probability theory to reason under conditions of uncertainty. We investigated the use of Bayesian networks for radiological decision support. A Bayesian network for the interpretation of mammograms (MammoNet) was developed based on five patient­history features, two physical findings, and 15 mammographic features extracted by experienced radiologists. Conditional­probability data, such as sensitivity and specificity, were derived from peer­reviewed journal articles and from expert opinion. In testing with a set of 77 cases from a mammography atlas and a clinical teaching file, MammoNet performed well in distinguishing between benign and malignant lesions, and yielded a value of 0.881 (± 0.045) for the area under the receiver operating characteristic curve. We conclude that Bayesian networks provide a potentially useful tool for mammographic decision support.

We describe the early stages of the development and validation of a Bayesian network to assist in the detection of breast cancer. MammoNet integrates mammo­ graphic findings, demographic factors, and physical examination to determine the probability of malignancy. Conditional probabilities were obtained from the medical literature and from expert opinion. Problems (and solutions) encountered while developing the model are discussed. MammoNet is imple­ mented as a knowledge base of rules; problem­specific networks are constructed using a Bayesian network construction algorithm. The model's performance was evaluated with 77 cases drawn from a textbook and a clinical teaching file; MammoNet performed well, and achieved an area under the receiver operating curve of 0.881 (± 0.045).

Decision­theoretic refinement planning is a new technique for finding optimal courses of ac­ tion. The authors sought to determine whether this technique could identify optimal strate­ gies for medical diagnosis and therapy. An existing model of acute deep venous thrombosis (DVT) of the lower extremities was encoded for analysis by the drips decision­theoretic refinement planning system. The encoding represented 6,206 possible plans. The drips planner used abstraction techniques to eliminate 5,551 plans (89%) from consideration with­ out examining them explicitly. It determined that, given the parameters specified, the most cost­effective management strategy was ``no tests, no treatment.'' This result differed from the published result of ``perform ultrasonography, treat if positive''; drips had revealed an error in the reference article's manually constructed decision trees. We conclude that decision­theoretic planning is useful for examining large medical decision problems, and that it can help reduce errors introduced by manual construction of decision trees.

We present an educational tool for bringing the in­ formation contained in a Bayesian network to the end user in an easily intelligible form. The banter shell is designed to tutor users in evaluation of hypotheses and selection of optimal diagnostic procedures. ban­ ter can be used with any Bayesian network contain­ ing nodes that can be classified into hypotheses, obser­ vations, and diagnostic procedures. We present algo­ rithms for determining optimal diagnostic procedures and for explanation generation.