Name
Abstract | ||
|---|---|---|
Clinical models of chronic disease characteristically must represent significant uncertainty in both the data input and inferences. This lack of determinism makes it especially difficult for system users to understand and have confidence in the models. This paper presents a representation for uncertainty and patient preferences that serves as a framework for graphical summary and computer-generated explanation of patient-specific clinical decision models. The implementation described is a computer decision aid designed to enhance the clinician-patient consultation process for patients with suspected angina pectoris. The generic angina model is represented as a Bayesian decision network, where the patient descriptors, probabilities, and preferences are treated as random variables. The initial distributions for these variables represent information on the population of patients with anginal symptoms, and the approach provides a method for efficiently tailoring the distributions to an individual patient. This framework also provides metrics for judging the importance of each variable in the model. The graphical interface uses this information to augment the display of a network representation of the model. Variables that are important for clinician-patient communication are highlighted in the graphical display of the network and included in the text explanation in printed patient-education materials. These techniques serve to keep the explanation of the patient's decision model concise, allowing the communication with the patient to focus on the most important aspects of the treatment decision. |
Year | DOI | Venue |
|---|---|---|
1992 | 10.1016/0933-3657(92)90027-M | Artificial Intelligence In Medicine |
Keywords | Field | DocType |
patient descriptors,clinical model,explanation,computer decision aid,decision model concise,patient education,decision theory,generic angina model,chronic disease,treatment decision,bayesian networks,patient-specific explanation,bayesian decision network,individual patient,patient preference,patient-specific clinical decision model | Data mining,Population,Determinism,Computer science,Bayesian network,Graphical user interface,Decision theory,Artificial intelligence,Decision model,Patient education,Machine learning,Bayesian probability | Journal |
Volume | Issue | ISSN |
4 | 3 | Artificial Intelligence In Medicine |
Citations | PageRank | References |
12 | 7.02 | 1 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Holly B. Jimison | 1 | 18 | 12.30 |
| L. M. Fagan | 2 | 64 | 16.11 |
| R. D. Shachter | 3 | 12 | 7.02 |
| E. H. Shortliffe | 4 | 12 | 7.02 |