INTRODUCTION: Depression is a global concern, with a significant number of people affected worldwide, particularly in low- and middle-income countries. The rising prevalence of depression emphasizes the importance of early detection and understanding the origins of such conditions. OBJECTIVE: This paper proposes a framework for detecting depression using a hybrid visualization approach that combines local and global interpretation. This approach aims to assist in model adaptation, provide insights into patient characteristics, and evaluate prediction model suitability in a different environment. METHODS: This study utilizes R programming language with the Caret, ggplot2, Plotly, and Dalex libraries for model training, visualization, and interpretation. Data from the NHANES repository was used for secondary data analysis. The NHANES repository is a comprehensive source for examining health and nutrition of individuals in the United States, and covers demographic, dietary, medication use, lifestyle choices, reproductive and mental health data. Penalized logistic regression models were built using NHANES 2015-2018 data, while NHANES 2019-March 2020 data was used for evaluation at the global-specific and local level interpretation. RESULTS: The prediction model that supports this framework achieved an average AUC score of 0.748 (95% CI: 0.743-0.752), with minimal variability in sensitivity and specificity. CONCLUSION: The built-in prediction model highlights chest pain, the ratio of family income to poverty, and smoking status as crucial features for predicting depressive states in both the original and local environments.

Cybernetics is many different things to many different people and thus it has been used within various scientific disciplines. The aim of the presented paper was to produce a snapshot of cybernetics information source production using bibliometric analysis. The results showed that the production of information sources has been increasing, with a significant but temporary decrease in 90 s. The distribution in the more prolific countries and institutions is similar to other scientific fields, with the exception of China, which is higher ranked than usual. Important cybernetic papers are published in high-ranking journals. The more frequent topics covered are engineering and computer sciences. Cybernetic research in 2013 was mostly funded by Australasian, U.S., and European funding agencies.

The aim of this paper is to present the results of abibliometrics analysis of the papers published in the Computer Based Medical Systems Symposium proceedings from 1993 to 2013. We present the dynamics of paper publishing patterns, changes in paper descriptive features and the dynamics of words frequencies. Some interesting patterns were revealed and the bibliometricsanalysis showed to be a very useful tool for quantitative analysis of paper production in given scientific sub field.

As the `third age' of human life becomes noticeably longer, the opportunity for elderly to obtain new skills reduces the tendency to consider this period of life as being disadvantaged. Hence, the fundamental aim of the project PRIMER-ICT was to educate older people in four participating countries (Slovenia, Ireland, UK and Austria) in Information and Communication Technologies (ICT) skills/practice by using an inter-generational and multisectoral approach empowering elderly to use ICT on everyday basis to improve their quality of live and to re-engage them in the society. To achieve this aim, students, primarily from health and ICT related fields have been recruited to become `trainers/teachers'. These students in turn have and will utilise acquired skills and knowledge to help train multipliers (community nurses, nurses in elderly homes, family members, volunteers from different sectors/ages, elderly), who in turn help training elderly.

UNLABELLED: Often, the most informative genes have to be selected from different gene sets and several computer gene ranking algorithms have been developed to cope with the problem. To help researchers decide which algorithm to use, we developed the analysis of gene ranking algorithms (AGRA) system that offers a novel technique for comparing ranked lists of genes. The most important feature of AGRA is that no previous knowledge of gene ranking algorithms is needed for their comparison. Using the text mining system finding-associated concepts with text analysis. AGRA defines what we call biomedical concept space (BCS) for each gene list and offers a comparison of the gene lists in six different BCS categories. The uploaded gene lists can be compared using two different methods. In the first method, the overlap between each pair of two gene lists of BCSs is calculated. The second method offers a text field where a specific biomedical concept can be entered. AGRA searches for this concept in each gene lists' BCS, highlights the rank of the concept and offers a visual representation of concepts ranked above and below it. AVAILABILITY AND IMPLEMENTATION: Available at http://agra.fzv.uni-mb.si/, implemented in Java and running on the Glassfish server. CONTACT: [email protected].

Technology is rapidly developing therefore educational institutions have to follow that progress and accede to the development of the flexible curriculums, which will be open to global health professionals. Therefore the Faculty of Health sciences together with European partners developed the GEROM on — line gerontological master curriculum which is interdisciplinary focused, supported by modern information communication technology (ICT) and aimed to health professionals to promote the health and well-being of elderly. The backgrounds to this project are demographic changes, technical advances in health care, availability of new pedagogies based on ICT, and pressure from professional bodies which are forcing health care educators to rethink how they construct and implement curricula. The innovative project contribution is the virtual environment for clinical practice, and the whole program implementation is based on adaptive, individualised and personalised blended learning. During the pilot execution students evaluated the program and were satisfied with both content and implementation.

Predicting protein solubility has gained lots of intention in the recent years and several descriptors have been defined to describe proteins in these works. Therefore, different feature selection methods have been used for selecting the most important attributes. An empirical study, that aims to explain the relationship between the number of samples and stability of seven different feature selection techniques for protein datasets, is presented.

BACKGROUND: Reproducibility of results can have a significant impact on the acceptance of new technologies in gene expression analysis. With the recent introduction of the so-called next-generation sequencing (NGS) technology and established microarrays, one is able to choose between two completely different platforms for gene expression measurements. This study introduces a novel methodology for gene-ranking stability analysis that is applied to the evaluation of gene-ranking reproducibility on NGS and microarray data. RESULTS: The same data used in a well-known MicroArray Quality Control (MAQC) study was also used in this study to compare ranked lists of genes from MAQC samples A and B, obtained from Affymetrix HG-U133 Plus 2.0 and Roche 454 Genome Sequencer FLX platforms. An initial evaluation, where the percentage of overlapping genes was observed, demonstrates higher reproducibility on microarray data in 10 out of 11 gene-ranking methods. A gene set enrichment analysis shows similar enrichment of top gene sets when NGS is compared with microarrays on a pathway level. Our novel approach demonstrates high accuracy of decision trees when used for knowledge extraction from multiple bootstrapped gene set enrichment analysis runs. A comparison of the two approaches in sample preparation for high-throughput sequencing shows that alternating decision trees represent the optimal knowledge representation method in comparison with classical decision trees. CONCLUSIONS: Usual reproducibility measurements are mostly based on statistical techniques that offer very limited biological insights into the studied gene expression data sets. This paper introduces the meta-learning-based gene set enrichment analysis that can be used to complement the analysis of gene-ranking stability estimation techniques such as percentage of overlapping genes or classic gene set enrichment analysis. It is useful and practical when reproducibility of gene ranking results or different gene selection techniques is observed. The proposed method reveals very accurate descriptive models that capture the co-enrichment of gene sets which are differently enriched in the compared data sets.

Data preprocessing is an important step in preparation of DNA microarray data for further analysis. There is a significant amount of genes that do not influence the final classification. One of the reasons to eliminate such genes is the increasing computational complexity of supervised machine learning methods, especially in modern microarray experiments with hundreds of samples. This empirical study aims to measure differences in classification performance when different numbers of gene expression measurements are removed in a preprocessing phase. Simple unsupervised gene selection based on variance level of genes across all samples was used to remove genes with extremely low level of variance. This study shows the importance of combining unsupervised and supervised feature selection techniques along with classification algorithm. It was shown that gene expression values removed using simple unsupervised gene selection method are not of significant importance to the final results of supervised gene selection followed by classification.

This paper presents an overview of past papers published at the CBMS symposiums from a content analysis point of view. A simple, yet effective word counting using Harvard Psycho-Social dictionary was used to estimate different aspects of sentiment that can be present even in scientific papers. Using simple statistics we uncover some of the very interesting trends in the last five CBMS symposiums. Additional to pure statistics we used some of the most advanced classification techniques to see if there are any significant differences in psycho-social texture of the accepted papers. It was also shown that building machine learning models on this kind of data can result in some very interesting generalizations of the underlying data.

The open source model has got a serious momentum over last few years and has proven its benefits also in complex (especially horizontal) applications. The time of open source in health care is yet to come though several successful stories has been marked already. There are some specifics of open source in health care that are investigated in the paper. On the basis of these specifics some recommendations are proposed to reach the interoperability and integration effect. A concrete example of O3 project is presented which is promoting open source adoption in e-health at regional, European and World-wide levels. This project aims to give a contribution to the development of e-health through the study of Healthcare Information Systems and the contemporary proposal of new concepts, designs and solutions for the management of health data in an integrated environment: hospitals, Regional Health Information Organizations and citizens (home-care, mobile-care and ambient assisted living).

The alcohol dependency is hard to diagnose since none of the existing laboratory markers has sufficient specificity and sensitivity. Therefore the goal of our study was to find better laboratory markers and / or their combinations. For that purpose the intelligent data analysis using the decision tree induction method was used. The results show that the combination of three or even two markers can prove alcohol dependency with almost 85% accuracy. However the remark has to be made that the induced decision tree offers a qualitatively different access to diagnostic evaluation of laboratory findings and varies from common practice, because it sets up its new and own borders and criteria what is the unlike from generally accepted or set up reference values. All selected markers are widely accessible, inexpensive and part of a routine laboratory tests.

A lot of research has been done in the field of assembling classifiers in ensembles and on the other hand selecting the most appropriate single classifiers for a given problem which was solved by meta-learning techniques. This paper presents application of recently proposed ensemble of classifiers called Rotation Forest to Grading meta-learning scheme, where it is used as one of the base classifiers and meta-level classifier at the same time. Our proposed Grading variation is compared to four widely used classifiers on 14 datasets from the domain of gene expression classification problems. Experimental evaluations show that using Rotation Forest at meta-level most significantly impacts the accuracy of Grading scheme and confirms that it can be used for estimation of classifiers regions of strong and weak classification.

The rate of survival for the out-of-hospital cardiac arrest (OHCA) is not encouraging. Quick and quality prognosis affects the OHCA outcome. Identification of important factors in prognosis can improve the survival rate. Intelligent data analysis of data obtained in a 4 year study of OHCA outcome in Maribor shows that arrival time of emergency medical team, witnessed arrest, bystander cardiopulmonary resuscitation and especially end-expired CO2are of great importance for the OHCA outcome.

From buying transport tickets (railway, bus, metro, airplane) up to shopping (books, tourist trips, clothes, food,..) and visiting cultural events (theater, cinema, concerts) or medical doctors (medicine prescriptions, illness, regular examination) activities connected to the information technology (IT) are needed and requested from professionals, participating in one or another above mentioned process. We would like to point out that every visit to the medical doctor nowadays requests the cooperation of IT. But are medical doctors and nurses really sufficiently equipped with knowledge about IT - which is in part their working environment? Without doubt, more knowledge about the possibilities, benefits, possible mistakes and their consequences related to IT is needed for being used adequately in the health services. In the paper we will present experiences which aim at moving those possible obstacles and targets in this field. In general, we will shortly present the health care and nursing study program. Additionally, we will focus on different for health care important IT areas. The paper will also discuss the experiences and assessments which are acquired during the last years by students.

Lymphoma is a broad term encompassing a variety of cancers of the lymphatic system. Lymphoma is differentiated by the type of cell that multiplies and how the cancer presents itself. It is very important to get an exact diagnosis regarding lymphoma and to determine the treatments that will be most effective for the patient's condition. Our work was focused on the identification of lymphomas by finding follicles in microscopy images provided by the Laboratory of Pathology in the University Hospital of Tenerife, Spain. Roughly we can divide our work in two stages: in the first stage we did image pre-processing and feature extraction, and in the second stage we used different rough set approaches for pixel classification. These results were compared to decision tree results we obtained earlier. Symbolic machine learning approaches are often neglected when looking for image analysis tools. The results we got are very promising and show that symbolic approaches can be successful in image analysis applications.

In this paper we present the results of an intelligent analysis of database which was gathered during e- learningproject called DISNET [1] in which a random sample of around 300 unemployed people collaborated. The intelligent data analysis using advanced methods for decision tree construction was used in order to try to find the main factors that influence a trainee's progress in knowing how to use e-materials. We also analysed how trainee's gender influences on the usage of e-materials.

Many software reliability studies attempt to develop a model for predicting the faults of a software module because the application of good prediction models provides important information on significant metrics that should be observed in the early stages of implementation during software development. In this article we propose a new method inspired by a multi-agent based system that was initially used for classification and attribute selection in microarray analysis. Best classifying gene subset selection is a common problem in the field of bioinformatics. If we regard the software metrics measurement values of a software module as a genome of that module, and the real world dynamic characteristic of that module as its phenotype (i.e. failures as disease symptoms) we can borrow the established bioinformatics methods in the manner first to predict the module behavior and second to data mine the relations between metrics and failures.

We present the results of a supervised approach for identification of follicular lymphomas in microscopy images. A new feature extraction approach is presented. The proposed discriminative features intend to emphasize the distinction among pixels on follicle contour. Additionally those features are used for supervised learning using classificational cellular automata (CCA) approach with the aim to obtain a general decision support model for classification of follicle contours on the microscopy images

Many different classification models and techniques have been employed on gene expression data. These computational methods are in rapid and continuous evolution and there is no clear consensus on which methods are best to cope with the complex microarray data analysis. Currently ensembles of classifiers are regarded as one of the best classification techniques as they can achieve excellent classification accuracy in comparison to single classifiers methods. One of their main drawbacks is their incomprehensibility. This paper addresses the important issue of the tradeoff between accuracy and comprehensibility when building ensembles and proposes a novel visual technique for interactive interpretation of the knowledge from the small ensembles consisting of only a few decision trees. This way we can achieve better accuracy compared to single classifier, but still maintain a certain level of comprehensibility in small ensembles. The results show that our small ensembles outperform the single classifiers and still retain comprehensibility. Our study also points out that in order to take advantage of our proposed method we need more effective small ensemble building techniques

Current software quality estimation models often involve the use of supervised learning methods for building a software fault prediction models. In such models, dependent variable usually represents a software quality measurement indicating the quality of a module by risk-basked class membership, or the number of faults. Independent variables include various software metrics as McCabe, Error Count, Halstead, Line of Code, etc... In this paper we present the use of advanced tool for data mining called Multimethod on the case of building software fault prediction model. Multimethod combines different aspects of supervised learning methods in dynamical environment and therefore can improve accuracy of generated prediction model. We demonstrate the use Multimethod tool on the real data from the Metrics Data Project Data (MDP) Repository. Our preliminary empirical results show promising potentials of this approach in predicting software quality in a software measurement and quality dataset.

Using a long-range correlation technique, the information density of human writings in Slovenian language relating to cultural, political, and technological changes was analyzed. It was noted that expanded opportunities for publishing and communication resulting from reduced costs, electronic media, etc. have caused “information inflation”—more words are used to describe the same amount of information.

In usual modern hospital architectural design we try to separate the clean and dirty corridor that should facilitate traffic flow of clean and dirty items. However that is not the case in the hospitals with older architectural design, where clean and dirty pathways flow through a single corridor systems. The most common solution which enables separation of clean and dirty pathways in such systems is introduction of time allocation for dirty and clean items transportation. Currently such schedules are fixed and do not allow quick changes in case of specific events. We propose a solution to the problem using multi-agent based system able to quickly adapt the schedule of transports.

Experts can make highly accurate decisions, because they have accumulated a lot of (background) knowledge about the problem with theirs past experience. Because experience is very subjective different experts propose different diagnosis and decision based on the same facts gathered with observation of a patient. Machine learning methods also poses background knowledge encoded in theirs induction algorithms. In this paper we present a method for modifying this background knowledge and can therefore produce different hypothesis on same observation that therefore exposes different aspects e.g. opinions of experts. We also present a method for combining these hypotheses in combined, hopefully highly accurate, hypothesis by using boosting and multimethod approach.

In this paper, medical decision support systems for TNM (tumor characteristics, lymph node involvement, and distant metastatic lesions) classification aiming to divide cancer patients to low and high risk patients are presented. In addition, the system also explained the decision in the form of IF-THEN rules and in this manner performed data mining and new knowledge discovery. The case studies show that the system is robust and not dependent on the database size and the noise. The accuracy was almost 80% which is comparable with the accuracy of physicians and much better then obtained with more conventional discriminant analysis (62% and 67%).

One of the most challenging tasks in the area of knowledge discovery is to express learned knowledge in a form, which can be understood by domain experts (e.g. medical experts). In the paper we present our approach to unsupervised learning using multivariate symbolic hybrid. Main advantage of multimethod symbolic hybrid is that learned knowledge is expressed in a form of symbolic rules. Learned knowledge is much more understandable to domain experts, which increases its value and makes it much easier to apply.

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.

In this paper we present the results of an intelligent analysis of osteoporosis database gathered during a longitudinal study in which a random sample of 100 women who had passed precautionary examinations for detection of osteoporosis over five years and were not referred for a definite medical diagnosis was pulled from the records. The intelligent data analysis using advanced methods for decision tree construction was used in order to try to find the main factors that can reduce the risk for development of osteoporosis in women. Most of the extracted knowledge confirmed known medical criteria that put women to risk for osteoporosis, however some new interesting patterns have also been shown.

Support vector machines are learning machines designed to automatically deal with the accuracy/generalisation trade-off, by minimizing an upper bound on the generalisation error provided by VC theory. That makes them very attractive for applications in different domains, especially in the field of medical diagnoses. In the practice however there are still few tuneable parameters, which need to be set to accomplish best accuracy/generalisation trade-off. There are also some important design choices to select appropriate kernel, which transforms non-liner separable problems into high dimensional possibly linear separable problems. In this paper the influence of kernels and kernel parameters on classification accuracy is presented We also focus on the representation of knowledge extracted from support vector machine to make it usable for medical decision support.

The use of intelligent systems and machine learning methods, capable of automatic decision making based on already solved cases, and data mining, are getting more and more popular. Here we are faced not only with technical problems, but also with limited confidence in machine learning techniques. In some cases methods that may explicitly show the deduction process are not powerful enough. One of the possibilities is to modify/improve the methods so that the users could easily follow the process of decision making. To solve this problem, a few years ago we started to develop a platform, which enables us to develop, test and use different kinds of hybrid methods. These are meant to combine the advantages of the integrated methods-e.g., power and knowledge representation-that contribute to the quality of the acquired knowledge. In this paper we present a way of using the developed platform in order to obtain new knowledge, based on results from neurophysiological measurements We are every pleased with the performance of our intelligent platform. The first results we obtained already show some improvement in comparison to classic machine learning approaches.

Modern medicine generates huge amounts of sensitive data. Data mining and knowledge discovery systems can be used to extract useful information/knowledge in order to to improve future diagnoses and reduce costs. On the other hand, when analysing data, the most recent methods, that extract the most accurate knowledge and decision models, should be used. This can be established with a service enabling quick access to newly developed methods. In this paper, a service for doing this, named IFSIMS (Internet Framework Service for Intelligent Medical Systems), is presented.

We study a new approach to rule induction from medical data sets with the help of physicians' assessment of the progressing solution in order to find new patterns in the available data. A method for the automatic extraction of rules is presented which is based on the evolutionary induction of decision trees and on automatic programming. The method is applied to a cardiovascular database. Several sets of rules are induced upon different groups of attributes which may possibly reveal the presence of some specific cardiovascular problems in young patients. The obtained rules are assessed by a physician in order to evaluate the strength of the developed knowledge discovery method.

A novel autonomous evolutionary algorithm for the construction of decision trees is presented, together with an analysis of different medical data sets. The algorithm's capability to self-adapt to a given problem is used as a measure to predict if some data set is just difficult or whether it is impossible to analyze. If a specific data set doesn't include enough or proper information for the creation of a good general decision model then over-fitting will occur. To detect over-fitting, we can use several existing techniques; the most common uses special test data that is excluded from the learning phase. On average, the autonomous algorithm produces very general solutions, or gives no solution if the data set is prone to over-fitting.

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.

We study the cross-learning approach on multiple databases to predict acute appendicitis. For the machine learning algorithm, our evolutionary method for inducing decision trees is used. The results of cross-learning are presented for the three different databases obtained in international projects regarding acute abdominal pain.

A decision tree is a good classifier with a transparent decision mechanism. Decision-tree building methods usually have problems in splitting the learning samples into more subsets, because of the nature of the tree. If the classification into such subsets is not possible, it is better to put the classification decision on to some other classifier. This leads to the introduction of a null classification, which simply means that no classification is possible in this step. This approach is sensible with evolutionary methods, as they can handle a number of trees simultaneously. In the process of construction, we have to address the problem of whether a classification is sensible. The performance of the proposed model has been tested on several data sets and the results presented on one such data set show its potential.

Decision trees are a relatively well-known and often-used intelligent tool for decision support. They are convenient for capturing knowledge from vast amounts of data. The result (or 'decision model') is represented in a hierarchical manner, where the significance or contribution of a single attribute to the final decision is shown very clearly. When the decision tree is built on real-world data, this data is more often numeric than discrete. Because it is human nature to use words rather than numbers to describe something, fuzzy logic theory was introduced to fill this gap. The description of attribute properties using the fuzzy logic approach is represented by a plausibility vector, where the coordinates between 0 and 1 show the plausibility of an attribute belonging to one of its subsets of possible attribute values. This is the way to successfully overcome the problem of boundary values between attribute subsets, where sharply determined boundaries can greatly affect the final result. In our system design laboratory, we have developed a software tool for building decision trees with a fuzzy heuristic function. The tool was used on data collected from a breastfeeding booklet, and the results were used for developing different advisory systems for health-care professionals as well as for breastfeeding support groups and mothers who have access to the Internet.

Decision trees have been already been successfully used in medicine, but as in traditional statistics, some hard real-world problems cannot be solved successfully using the traditional method of induction. In our experiments, we tested various methods for building univariate decision trees in order to find the best induction strategy. On a hard real-world problem concerning orthopaedic fracture data, with 2637 cases described by 23 attributes and a decision with three possible values, we built decision trees with four classical approaches, with a hybrid approach (where we combined neural networks and decision trees) and with an evolutionary approach. The results show that all the approaches had problems with either accuracy or decision tree size. The comparison shows that the best compromise in hard real-world decision-tree building is the evolutionary approach.

Decision support systems that help physicians are becoming a very important part of medical decision making. They are based on different models and the best of them are providing an explanation together with an accurate, reliable and quick response. One of the most viable among these models are decision trees, already successfully used for many medical decision making purposes. Although effective and reliable, the traditional decision tree construction approach still contains several deficiencies. The real-world problem that was a background of our research has a special structure. According to the same attributes we have to determine five different decisions. We decided for two different approaches: the traditional decision trees and a completely new approach of multiple-outcome decision making where we get a decision vector as a result of the decision tree. For constructing such decision trees we used evolutionary programming and we have developed a tool named DecRain. A comparison between developed models and obtained results has shown that our approach can be considered as a good choice for different kinds of real-world medical decision making.

Mechanical systems increase our physical abilities (cranes to lift vast amounts, telescopes to see farther, etc.), but intelligent systems are power tools for heavy lifting in the information world-they complement, extend, and amplify our ability to think and solve problems. In this paper we introduce some thoughts and ideas about intelligent medical system design and the use of MetaMet. A specific design approach constructed with MetaMet is presented and discussed.

Medical decision making is one of the most demanding tasks in modern medicine. Based on a huge amount of solved cases and rapidly growing number of new findings, medical decision making is supposed to become more and more reliable and effective. However, physicians and medical experts are facing a new arising problem - information overload which they are not able to overcome. A solution might be found in decision support systems with intelligent information processing abilities that help to process a huge amount of data and suggest a possible decision for each new case. Regarding the simplicity of decision trees and effectiveness of evolutionary programming techniques we developed a decision support system based on automatic programming and used it to solve the mitral valve prolapse classification problem.

“Long-range power low correlation” (LRC) is defined as a maximal propagation distance of the effect of some disturbance within a system found in many systems that can be represented as strings of symbols. LRC between characters has also been identified in natural language texts. The aim of this article is to show that long-range power law correlation can also be found in computer programs, meaning that some common laws hold for both natural language texts and computer programs. This fact enables one to draw parallels between these two different types of human writings, and also enables one to measure the differences between them.

In paediatric cardiology, a database and its supporting information system are indispensable in order to conduct more extensive and detailed investigations. Diagnostic decisions during the diagnostic process in congenital heart disease (CHD) must be supported by a suitable decision support system. In the majority of children with CHD, the diagnosis should be made, if possible, before the complete diagnostic procedure has been accomplished, with maximal information (probability of correct diagnosis = 1.00), minimal risk for the patient and the doctor (the minimal needed number of steps, especially for highly invasive methods) and minimal cost-benefit ratio. We developed a tool called DIAPRO (DIAgnostic PRocess Optimisation) that supports all activities concerned with the management of the diagnostic process and the management of the medical records database, but its main strength is in the decision-making support concerning the optimisation of the diagnostic process. It enables the paediatrician to perform cardiological examinations in a maximally rationalised manner.

Efficient information exchange is the principle on which successful pre-hospital patient care should be based, especially when emergency medical services (EMS) are being invoked. For quick arrival at the scene, clear and reliable information at the right time is often crucial for patient survival. All aspects of pre-hospital care require constant and efficient communications. In addition to using routine radio communications, the EMS team must organize and present patient information through spoken communications and in written reports. Voice transmission over radio channels can be limited because of weak signals or language problems, and it also needs human operators. This paper presents a prototype for data exchange over a radio network. Our prototype has shown that, by using it, we can simply, quickly and accurately transfer information over long distances.

Medical knowledge is expanding rapidly. The appearance of information technology has initiated the possibility to manage that knowledge more effectively. Indeed, medical intelligent systems are or will rapidly become key components in both: (1) information management, and (2) effective/efficient decision making concerning diagnosing, planning and treating.

We present an intelligent search tool, which we developed in order to automate search and evaluation of Web sites. We used TFIDF heuristics to determine term frequency and decision trees to evaluate the quality of sites. The training set for the decision tree contained manually evaluated Web sites. Each Web site was described by the combination of various attributes, complexity metrics and the evaluation. The intelligent search tool is equipped with a user friendly interface, which enables people to exploit the tool to its limits with minimum effort, in their quest for information. For testing purposes, we looked for sites with telemedical content. The set of sites, which was the result of using intelligent search tool was evaluated by a group of students.

A different view of software complexity based on long-range correlations is presented in this paper. Three different methods are proposed first and then used in the analysis of C , FORTRAN, and Pascal programs. All methods being used indicate the presence of long-range correlations in programs, but every method gives different results. The presence of longrange correlations denotes that we can regard the complexity of computer programs from a different perspective and use it as an advanced software metric.

Numerous development methods, based on the variety of paradigms, have been proposed over the years. Of these very few were successfully applied in the development of computer based systems. Both generic and more specific methods have been designed and constantly adapted and extended to meet the changing needs. A "metaapproach" supporting adaptation and extensions called method engineering, has been proposed recently. Method engineering is a very promising, but at the same time very young paradigm, and consequently not yet very well defined. Because of its encouraging features the author decided to analyze the possible application of method engineering in medical system design. In this manner the author had to clarify some points about method engineering and redefine it to be beneficial in medical design situations. The aim of this paper is to present how the Checklands Soft System Methodology has been used in the accomplishment of above tasks.

It has become evident that decision support systems play a very important role in medical decision making, particularly in those where decision must be made effectively and reliably. A syndrome is a combination of signs and/or symptoms that forms a distinct clinical picture indicative of a particular disorder. Here, the authors present a decision support system based on decision tree concept and automated learning which help medical personnel deciding about syndromes on a base of anomalies.

Every-day routine responsibilities of medical staff can be enormous. The appearance of new computer-based information technology has eased these activities enormously and enabled medical staff to perform their work more efficiently and effectively. One possible application of computer technology in paediatric cardiology, where examinations can be very expensive, is the optimisation of the diagnostic process in a manner to minimise the number of examinations, and to reduce costs and the risk to the patients. In this paper, we present an information system which supports diagnostic process optimisation (DIAPRO) and the approach used to design it.>

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 following: 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 proper trained experts armed with proper criteria to evaluate PMV almost 100%. Unfortunately there are some problems concerned with the use of echocardiography. We have decided to start a research project aimed to find new criteria and enable general practitioners to evaluate the PMV using conventional methods and to select potential patients from the general population. To empower one to perform needed activities we developed a computer tool called ROSE (Computerised Prolaps Syndrome Determination) based on algorithms of automatic learning. This tool supports the definition of new criteria and the selection of potential PMV-patients.>

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 ChIld's 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.>

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.>