Przemyslaw Wiktor Pardel

Prediction of Coronary Arteriosclerosis in Stable Coronary Heart Disease

The aim of the study was to assess the usefulness of classification methods in recognizing cardiovascular pathology. From the medical point of view the study involves prediction of coronary arteriosclerosis presence in patient with stable angina using clinical data and electrocardiogram (ECG) Holter monitoring records. On the grounds of these findings the need for coronary interventions is determined. An approach to solving this problem has been found in the context of rough set theory and methods. Rough set theory introduced by Zdzislaw Pawlak during the early 1980s provides the foundation for the construction of classifiers. From the rough set perspective, classifiers presented in the paper are based on a decision tree calculated on the basis of the local discretization method. The paper includes results of experiments that have been performed on medical data obtained from II Department of Internal Medicine, Jagiellonian University Medical College, Krakow, Poland.

Predicting the presence of serious coronary artery disease based on 24 hour Holter ECG monitoring

The purpose of this study was to evaluate the usefulness of classification methods in recognizing a cardiovascular pathology. Based on clinical and electrocardiographic (ECG) Holter data we propose a method for predicting a coronary stenosis demanding revascularization in patients with a diagnosis of a stable coronary heart disease. A possible solution of this problem has been set in a context of rough set theory and methods. The rough set theory introduced by Zdzisław Pawlak during the early 1980s provides a foundation for the construction of classifiers. From the rough set perspective, classifiers presented in the paper are based on a decision tree calculated on a basis of a local discretization method, related to the problem of reducts computation. We present a new modification of a tree building method which emphasizes the discernibility of objects belonging to decision classes indicated by human experts. The presented method may be used to assess the need for the coronary revascularization. The paper includes results of experiments that have been performed on medical data obtained from Second Department of Internal Medicine, Collegium Medicum, Jagiellonian University, Kraków, Poland.

RDBMS Model for Scientific Articles Analytics

We present the relational database schema aimed at efficient storage and querying parsed scientific articles, as well as entities corresponding to researchers, institutions, scientific areas, et cetera. An important requirement in front of the proposed model is to operate with various types of entities, but with no increase of schema’s complexity. Another aspect is to store detailed information about parsed articles in order to conduct advanced analytics in combination with the domain knowledge about scientific topics, by means of standard SQL and RDBMS management. The overall goal is to enable offline, possibly incremental computation of semantic indexes supporting end users via other modules, optimized for fast search and not necessarily for fast analytics, as well as direct ad-hoc SQL access by the most advanced users.

Classifiers Based on Data Sets and Domain Knowledge: A Rough Set Approach

The problem considered is how to construct classifiers for approximation of complex concepts on the basis of experimental data sets and domain knowledge that are mainly represented by concept ontology. The approach presented in this chapter to solving this problem is based on the rough set theory methods. Rough set theory introduced by Zdzislaw Pawlak during the early 1980s provides the foundation for the construction of classifiers. This approach is applied to approximate spatial complex concepts and spatio-temporal complex concepts defined for complex objects, to identify the behavioral patterns of complex objects, and to the automated behavior planning for such objects when the states of objects are represented by spatio-temporal concepts requiring approximation. The chapter includes results of experiments that have been performed on data from a vehicular traffic simulator and the recent results of experiments that have been performed on medical data sets obtained from Second Department of Internal Medicine, Jagiellonian University Medical College, Cracow, Poland. Moreover, we also describe the results of experiments that have been performed on medical data obtained from Neonatal Intensive Care Unit in the Department of Pediatrics, Jagiellonian University Medical College, Cracow, Poland.

SONCA: Scalable Semantic Processing of Rapidly Growing Document Stores

Scientific data constitutes a great asset. However, its volume is far bigger than any human can comprehend. Therefore, automatic analytical, search and indexing solutions are called for. In this paper we present the architecture and the data model of such a system. SONCA (Search based on ONtologies and Compound Analytics) is a platform to implement and exploit intelligent algorithms identifying relations between various types of objects (publications, inventions, scientists and institutions). The results of these algorithms can be used to build semantic search engines but also can be fed into further analytical algorithms in order to find even more associations.We also show experimental evaluation of the performance of SONCA. Its results are promising and we argue that SONCA’s architecture is robust.

Automatic Medical Objects Classification Based on Data Sets and Domain Knowledge

This paper describes the approach for automatic identifying organs from a medical CT imagery. Main assumption of this approach is the use of data sets and domain knowledge. We apply this approach to automatic classification of chest organs (trachea, lungs, bronchus) and present the results to demonstrate their usefulness and effectiveness. The paper includes the results of experiments that have been performed on medical data obtained from II Department of Internal Medicine, Jagiellonian University Medical College, Krakow, Poland. The experimental results showed that the approach is promising and can be used in the future to support solving more complex medical problems.

The Method for Describing Changes in the Perception of Stenosis in Blood Vessels Caused by an Additional Drug

The decision making depends on the perception of the world and the proper identification of objects. The perception can be modified by various factors, such as drugs or diet. The purpose of this research is to study how the disturbing factors can influence the perception. The idea was to introduce the description of the rules of these changes. We propose a method for evaluating the effect of additional therapy in patients with coronary heart disease based on the tree of the impact. The leaves of the tree provide cross-decision rules of perception changes which could be suggested as a solution to the problem of predicting changes in perception. The problems considered in this paper are associated with the design of classifiers which allow the perception of the object in the context of information related to the decision attribute.

A two-level classifier for automatic medical objects classification

The goal of this paper is to describe the approach for automatic identifying human organs from a medical CT images and discuss results of its comparison to different classification methods. The main premise of this approach is the use of data sets together with the relevant domain knowledge. We test our approach on multiple CT images of chest organs (trachea, lungs, bronchus) and demonstrate usefulness and effectiveness of the resulting classifications. The presented approach can be used to assist in solving more complex medical problems.