Bruno Stiglic

Decision Trees: An Overview and Their Use in Medicine

In medical decision making (classification, diagnosing, etc.) there are many situations where decision must be made effectively and reliably. Conceptual simple decision making models with the possibility of automatic learning are the most appropriate for performing such tasks. Decision trees are a reliable and effective decision making technique that provide high classification accuracy with a simple representation of gathered knowledge and they have been used in different areas of medical decision making. In the paper we present the basic characteristics of decision trees and the successful alternatives to the traditional induction approach with the emphasis on existing and possible future applications in medicine.

Metaparadigm: A soft and situation oriented MIS design approach

Every-day routine work performed by medical staff can be enormous, thus continuing education, diagnostic assistance, medical research or literature searches remains only an aspiration for them. The appearance of the computer-based information technology has initiated the possibility to significantly ease these routine activities and enable the medical staff to devote more time to enhanced creative work. However, only when medical staff can use computers reliably, effectively, democratically, easily, and in natural, intuitive fashion, medical information system (MIS) will be used enthusiastically and favourably. In that manner special attention must be dedicated to MIS design. The aim of our paper is to introduce the new (M)IS design approach called metaparadigm and show its applicability to MIS design. A metaparadigm is a holistic, participative and systemic approach, incorporating design activities needed to construct both the (M)IS design paradigm and the selected (M)IS. It is our deepest belief that metaparadigms employment can enhance many IS design weaknesses and result in the successful medical information systems design and use.

Software crisis and new development paradigms or how to design reliable medical software

The extended operational paradigm (EOP) is a software development paradigm for dealing with the emerging software crisis in the field of medical software development. This crisis is a result of using the waterfall paradigm, which neglects many dimensions of system design. The EOP paradigm is based on the multidimensional, multiview, multilingual, and user-oriented (M/sup 3/UO) framework. A CASE tool called the extended operational approach (EOA), developed to support EOP, is presented. EOA is based on JSP, SML, and spreadsheet software. EOP was successfully used in the development of a medical decision and information support system. Experience with using EOA has shown that it reduces the complexity of the specification. The semantic gap between users and development groups, and the effort results in increased productivity, user satisfaction, and more reliable systems.<<ETX>>

Mining diabetes database with decision trees and association rules

Searching for new rules and new knowledge in problem areas, where very little or almost none previous knowledge is present, can be a very long and demanding process. In our research we addressed the problem of finding new knowledge in the form of rules in the diabetes database using a combination of decision trees and association rules. The first question we wanted to answer was, if there are significant differences in sets of rules both approaches produce, and how rules, produced by decision trees behave, after being a subject of filtering and reduction, normally used in association rule approaches. In order to accomplish that, we had to make some modifications to both the decision tree approach and association rule approach. From the first results we can conclude, that the sets of rules, built by decision trees are much smaller than the sets created by association rules. We could also establish, that filtering and reduction did not effect the rules derived from decision trees in the same scale as association rules.

How to evaluate object-oriented software development?

Adoption of object-oriented technology by the software industry is to a large extent interfered with a lack of appropriate evaluation criteria. Measures of object-oriented systems are necessary in order to realize and achieve expected benefits and advantages of object technology. This paper presents some evaluation criteria, measures and metrics, suitable for object-oriented software development. The implemented prototype of OOMetTool that provides an automatic support for metric data gathering is described and some initial findings are presented.

Improved medical decision support with multimethod approach

An enormous proliferation of databases in almost all areas of human life has created great need to develop tools for automatic knowledge extraction. Extracted knowledge can be used for categorizing, organizing or predictive purposes. One of the problems encountered is how to make a good induction with good generalization and knowledge representation what is especial important in medical domain. Although the research filed is very active, it is mainly focused on a specific method or on a specific combination of those methods. In this paper a multimethod approach is presented. This approach unlike other conventional hybrid approaches applies different methods on the same knowledge base where each method may contain inherent limitations with the expectation that the combined multiple methods may produce better results. It also addresses unbalanced nature of medical data.

Does Size Really Matter—Using a Decision Tree Approach for Comparison of Three Different Databases from the Medical Field of Acute Appendicitis

Decision trees have been successfully used for years in many medical decision making applications. Transparent representation of acquired knowledge and fast algorithms made decision trees one of the most often used symbolic machine learning approaches. This paper concentrates on the problem of separating acute appendicitis, which is a special problem of acute abdominal pain, from other diseases that cause acute abdominal pain by use of an decision tree approach. Early and accurate diagnosing of acute appendicitis is still a difficult and challenging problem in everyday clinical routine. An important factor in the error rate is poor discrimination between acute appendicitis and other diseases that cause acute abdominal pain. This error rate is still high, despite considerable improvements in history-taking and clinical examination, computer-aided decision-support, and special investigation such as ultrasound. We investigated three databases of different size with ca ses of acute abdominal pain to complete this task as successful as possible. The results show that the size of the database does not necessary directly influence the success of the decision tree built on it. Surprisingly we got the best results from the decision trees built on the smallest and the biggest database, where the database with medium size (relative to the other two) was not so successful. Despite this we were able to produce decision tree classifiers that were capable of producing correct decisions on test data sets with accuracy up to 84%, sensitivity to acute appendicitis up to 90%, and specificity up to 80% on the same test set.

The application of structured spreadsheet modeling in medicine

Abstract The effective use of computers in medical praxis is limited first by difficulties of medical staff in learning and using conventional design paradigms and second by the variety of medical applications. Only when medical staff can use computers quickly, easily, and in a natural, intuitive fashion will medical computer-supported systems be used enthusiastically and successfully. In the present paper we show that structured spreadsheet modeling, which is a combination of structured modeling and spreadsheet software, can be used effectively and efficiently in the process of designing, testing, and evaluating various classes of medical models.

Machine-Learning with cellular automata

As the possibility of combining different classifiers into Multiple Classifier System (MCS) becomes an important direction in machine-learning, difficulties arise in choosing the appropriate classifiers to combine and choosing the way for combining their decisions. Therefore in this paper we present a novel approach – Classificational Cellular Automata (CCA). The basic idea of CCA is to combine different classifiers induced on the basis of various machine-learning methods into MCS in a non-predefined way. After several iterations of applying adequate transaction rules only a set of the most appropriate classifiers for solving a specific problem is preserved. We empirically showed that the superior results compared to AdaBoost ID3 are a direct consequence of self-organization abilities of CCA. The presented results also pointed out important advantages of CCA, such as: problem independency, robustness to noise and no need for user input.

Tracing and controlling child's development using COCIDIN

Two activities are essential for the successful care of children, namely adequate trace (measurement, recording, trend and pattern calculations) and control (measurement, comparison and possible mediation) of their development. To enable efficient trace and control we should follow as more as possible various development parameters. The intention of this paper is to present the COCIDIN (Computerized cOntinued ChIlds Development INdex) and its design. COCIDIN is a numeric index founded on observations of ten important parameters and can be used as a suitable base for above activities.<<ETX>>