ROSE: decision trees, automatic learning and their applications in cardiac medicine.
Computerized information systems, especially decision support systems, have acquired an increasingly important role in medical applications, particularly in those where important decisions must be made effectively and reliably. But the possibility of using computers in medical decision making is limited by many difficulties, including the complexity of conventional computer languages, methodologies, and tools. Thus a conceptual simple decision making model with the possibility of automating learning should be used. In this paper, we introduce a cardiological knowledge-based system based on the decision tree approach supporting the mitral valve prolapse determination. Prolapse is defined as the displacement of a bodily part from its normal position. The term mitral valve prolapse (PMV), therefore, implies that the mitral leaflets are displaced relative to some structure, generally taken to be the mitral annulus. The implications of the PMV are: disturbed normal laminar blood flow, turbulence of the blood flow, injury of the chordae tendinae, the possibility of thrombus's composition, bacterial endocarditis, and, finally, hemodynamic changes defined as mitral insufficiency and mitral regurgitation. Uncertainty persists about how it should be diagnosed and about its clinical importance. It is our deep belief that the echocardiography enables properly trained expert armed with proper criteria to evaluate PMV almost 100%. But, unfortunately, there are some problems concerned with the use of echocardiography. With this in mind, we have decided to start a research project aimed at finding new criteria and enabling the general practitioner to evaluate the PMV using conventional methods and to select potential patients from the general population. To empower doctors to perform needed activities, we have developed a computer tool called ROSE (computeRized prOlaps Syndrome dEtermination) based on algorithms of automatic learning. This tool supports the definition of new criteria and the selection of potential PMV-patients. The ROSE is based on concepts of decision trees and automatic learning. The decisions and learning process can be presented with an easily visualized two dimensional model; thus decision trees are straightforward to build and interpret. Decision trees use different object attributes to classify different subsets of objects. (Their great advantage is that they don't use a fixed number of predetermined attributes.) In the decision tree approach, the members of a set of objects are classified as either positive or negative instances (in our case patients with PMV Syndrome or without it). Candidate attributes that may possibly describe the concept are then outlined. A decision tree construction tool uses outlined attributes to formulate the appropriate decision tree that identifies all positive instances of the underlying concept according to objects with known classification. (In our case the classification is done with the echo examination). The first set of objects used for the tree generation is usually called the training set. This decision tree characterization next becomes a basis: 1) forecasting whether an object previously unseen is a positive or negative instance of the concept being modeled, and 2) the hierarchical representation of the most important attributes of the concept being investigated. Our main interest and idea is to discover symptoms, syndromes, and illnesses related to PMV that can be distinguished by general practitioners in their everyday job and which should help them to identify possible PMV candidate patients. To this end, we constructed a computerized tool called ROSE. According to the principles presented above, we first taught ROSE using the sample of 400 examined volunteers. ROSE, considering that the clinical PMV diagnosis is practically not researched, is relatively successful. (abstract truncated)