Ágnes Vathy-Fogarassy

Ágnes Vathy-Fogarassy
University of Pannonia, Veszprém ·  Department of Computer Science and Systems Technology

PhD

About

63
Publications
13,245
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320
Citations
Introduction
Ágnes Vathy-Fogarassy received the Ph.D. degree in Information Science in 2009 from the Eötvös Lóránd University, Hungary. Currently, she is a full-time associate professor and the Head of the Department of Computer Science and Systems Technology at the University of Pannonia. Her research interest focuses on Medical Informatics, Data Science, Data Mining, Predictive Analytics, Network Analysis, and Databases. Furthermore, she is the Head of the Healthcare Business Analytics Research and Development Centre too.
Additional affiliations
September 1998 - present
University of Pannonia, Veszprém
Education
September 1999 - January 2007
Eötvös Loránd University
Field of study
  • Computer Science
September 1995 - June 1998
Unversity of Pannonia
Field of study
  • Computer science

Publications

Publications (63)
Article
Despite the continuous growth and the widespread support of renewable energy sources, solar and wind power plants pose new challenges for Transmission System Operators and Distribution System Operators. Their uncontrollability limits their applicability; therefore, to encourage their further growth, fundamental modifications are needed. The researc...
Article
Full-text available
An essential criterion for the proper implementation of case-control studies is selecting appropriate case and control groups. In this article, a new simulated annealing-based control group selection method is proposed, which solves the problem of selecting individuals in the control group as a distance optimization task. The proposed algorithm pai...
Article
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Background Public healthcare is a complex domain with many actors and highly variable protocols, which makes traditional process mining tools less effective and calls for specialized methods. Aim The objective of the work was to develop a generally applicable process mining methodology to explore care processes related to diseases. Methods The pr...
Conference Paper
In recent years the number of electronic systems dramatically increased in vehicles. In the field of autonomous braking the Electronic Stability Control (ESC) is one of the most important braking functions. Two of the biggest challenges of the development of ESC are the validity of the inputs and logic of the intervention. In this paper the initial...
Article
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Aim: We aimed to analyse the long-term effects of the biosimilar bids of NEAK regarding GCSF and EPO drugs. Data and Methods: Our analysis is based on the data derived from the nationwide pharmaceutical database of NEAK. The treatment days and reimbursement figures of 12 months periods has been compared, from 01.07.2011-30.06.2014. and 01.07.2017.-...
Article
Aims: After enhancing the survivorship of cancers, the impact of cardiovascular diseases on mortality is increasing among cancer patients. However, anticancer therapies pose a higher cardiovascular risk to patients. As prevention against cancer therapy-induced cardiomyopathy has yet to be explored, the preventive ability of concomitant cardiovascu...
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Although in observational studies, propensity score matching is the most widely used balancing method, it has received much criticism. The main drawback of this method is that the individuals of the case and control groups are paired in the compressed one-dimensional space of propensity scores. In this paper, such a novel multivariate weighted k-ne...
Article
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Background: Patient appointment scheduling is one of the main challenging tasks in the healthcare administration and is constantly in the focus of theoretical researches. Objectives: The aim of this study was to investigate the applicability of the P-graph (Process graph) methodology to find the n-best alternative for patient's scheduling. Meth...
Article
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Background: Processing of free text written medical texts involves many difficulties arising from typographical errors, synonyms, and abbreviations occurring in the texts. Methods: In this study, the applicability of the most common string similarity measures were analyzed and compared for the keyword-based medical text search. Results: The us...
Article
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Introduction: The incidence of dilated cardiomyopathy after anthracycline chemotherapy is mainly influenced by anthracycline cumulative dose. Previous researches showed doxorubicin treatment under cumulative dose of 450 mg/m2 associated with a low incidence of heart failure. Nowadays, doxorubicin is administered with a lower dose, the development...
Chapter
In this chapter, the functionality of the proposed Octave- and MATLAB-compatible network-based observability and controllability analysis of dynamical systems (NOCAD) toolbox is introduced. This toolbox provides a set of methods, which enables the structural controllability and observability analysis of dynamical systems presented in the earlier ch...
Chapter
This work proposes a network science-based analysis tool for the qualification of controllability and observability of HENs. With the proposed methodology, the main characteristics of HEN design methods are determined, the effect of structural properties of HENs on their dynamical behaviour is revealed, and the potentials of the network-based HEN r...
Chapter
However, the resulting proportions of driver and sensor nodes are particularly small when compared to the size of the system, and although structural controllability and observability is ensured, the system demands additional drivers and sensors to provide the small relative degree needed for fast and robust process monitoring and control. In this...
Chapter
This chapter introduces the reader to the network-based analysis of the controllability and observability of dynamical systems and draws attention to the importance of dynamics between the state variables.
Article
Full-text available
The network science-based determination of driver nodes and sensor placement has become increasingly popular in the field of dynamical systems over the last decade. In this paper, the applicability of the methodology in the field of life sciences is introduced through the analysis of the neural network of Caenorhabditis elegans. Simultaneously, an...
Conference Paper
Full-text available
Recently a growing interest can be observed in the field of financial forecasting and especially in the field of cryp-tocurrency market forecasting. This proved to be an outstandingly complex problem because of the many special characteristics of these markets. Making accurate predictions requires the proper usage and fine tuning of the most modern...
Conference Paper
Full-text available
Case-control studies rely on the fact, that individuals of the case group are similar to the individuals of the control group, except for the feature under investigation. However, measuring the similarity of the case group and the control group is a complex task. The widely applied statistical methods compare the distributions of the characteristic...
Article
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According to the Industry 4.0 paradigms, the balancing of stochas-tic production lines requires easily implementable, flexible and robust tools for task to workstations assignment. An algorithm that calculates the performance indicators of the production line based on the convolution of the empirical density distribution functions of the working ti...
Article
Full-text available
The network science-based determination of driver nodes and sensor placement has become increasingly popular in the field of dynamical systems over the last decade. In this paper, the applicability of the methodology in the field of life sciences is introduced through the analysis of the neural network of Caenorhabditis elegans. Simultaneously, an...
Article
Full-text available
The design and retrofit of Heat Exchanger Networks (HENs) can be based on several objectives and optimisation algorithms. As each method results in an individual network topology that has a significant effect on the operability of the system, control-relevant HEN design and analysis are becoming more and more essential tasks. This work proposes a n...
Article
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As there is no consensus about how to store the results of echocardiography examinations, information extraction from them is a non-trivial task. Successful named entity recognition (NER) is key to getting access to the stored information and the process of identification has been recognized as a bottleneck in text mining. Our goal was to develop a...
Article
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Network science-based analysis of the observability of dynamical systems has been a focus of attention over the past five years. The maximum matching-based approach provides a simple tool to determine the minimum number of sensors and their positions. However, the resulting proportion of sensors is particularly small when compared to the size of th...
Article
Full-text available
The control-relevant design and analysis of Heat Exchanger Networks (HENs) is an essential issue in terms of the design and intensification of sustainable production systems. The structural controllability and observability of HENs should be studied based on their dynamical model. Recently, a maximum matching based algorithm was developed to determ...
Conference Paper
Full-text available
According to the concept of Industry 4.0, shop floor control and optimization should be more and more autonomous and integrated. In the age of digital transformation, human operators are still applied in manufacturing processes, so the uncertainty of their tasks times cannot be ignored during scheduling and line balancing. To provide accurate and r...
Article
Full-text available
Logistic regression based propensity score matching is a widely used method in case-control studies to select the individuals of the control group. This method creates a suitable control group if all factors affecting the output variable are known. However, if relevant latent variables exist as well, which are not taken into account during the calc...
Conference Paper
Full-text available
The huge amount of data stored in healthcare databases allows wide range possibilities for data analysis. In this article, we present a novel multilevel analysis methodology to generate and analyze sequential healthcare treatment events. The event sequences can be generated on different abstraction levels automatically from the source data, and so...
Conference Paper
Full-text available
Retrospective studies suffer from drawbacks such as selection bias. As the selection of the control group has a significant impact on the evaluation of the results, it is very important to find the proper method to generate the most appropriate control group. In this paper we suggest two nearest neighbors based control group selection methods that...
Article
Full-text available
Integration of data stored in heterogeneous database systems is a very challenging task and it may hide several difficulties. As NoSQL databases are growing in popularity, integration of different NoSQL systems and interoperability of NoSQL systems with SQL databases become an increasingly important issue. In this paper, we propose a novel data int...
Article
Full-text available
Network theory based controllability and observability analysis have become widely used techniques. We realized that most applications are not related to dynamical systems, and mainly the physical topologies of the systems are analysed without deeper considerations. Here, we draw attention to the importance of dynamics inside and between state vari...
Article
Full-text available
The huge amount of data stored in healthcare databases allows wide range possibilities for data analysis. In this article, we present a novel multilevel analysis methodology to generate and analyze sequential healthcare treatment events. The event sequences can be generated on different abstraction levels automatically from the source data, and so...
Article
Full-text available
Retrospective studies suffer from drawbacks such as selection bias. As the selection of the control group has a significant impact on the evaluation of the results, it is very important to find the proper method to generate the most appropriate control group. In this paper we suggest two nearest neighbors based control group selection methods that...
Article
Full-text available
Production flow analysis includes various families of components and groups of machines. Machine-part cell formation means the optimal design of manufacturing cells consisting of similar machines producing similar products from a similar set of components. Most of the algorithms reorders of the machine-part incidence matrix. We generalize this clas...
Conference Paper
Full-text available
We present a modified fuzzy c-medoid algorithm to find central objects in graphs. Initial cluster centres are determined by graph centrality measures. Cluster centres are fine-tuned by minimizing fuzzy-weighted geodesic distances calculated by Dijkstra’s algorithm. Cluster validity indices show significant improvement against fuzzy c-medoid cluster...
Book
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This work presents a data visualization technique that combines graph-based topology representation and dimensionality reduction methods to visualize the intrinsic data structure in a low-dimensional vector space. The application of graphs in clustering and visualization has several advantages. A graph of important edges (where edges characterize r...
Chapter
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The way how graph-based clustering algorithms utilize graphs for partitioning data is very various. In this chapter, two approaches are presented. The first hierarchical clustering algorithm combines minimal spanning trees and Gath-Geva fuzzy clustering. The second algorithm utilizes a neighborhood-based fuzzy similarity measure to improve k-neares...
Chapter
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In this chapter we give an overview of classical dimensionality reduction and graph based visualisation methods that are able to uncover hidden structure of high dimensional data and visualise it in a low-dimensional vector space.
Chapter
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Compact graph based representation of complex data can be used for clustering and visualisation. In this chapter we introduce basic concepts of graph theory and present approaches which may generate graphs from data. Computational complexity of clustering and visualisation algorithms can be reduced replacing original objects with their representati...
Article
Full-text available
As data analysis tasks often have to deal with complex data structures, the nonlinear dimensionality reduction methods play an important role in exploratory data analysis. In the literature a number of nonlinear dimensionality reduction techniques have been proposed (e.g. Sammon mapping, Locally Linear Embedding). These techniques attempt to preser...
Article
Full-text available
In practical data mining tasks high-dimensional data has to be analyzed. In most of the cases it is very informative to map and visualize the hidden structure of a complex data set in a low-dimensional space. In this paper a new class of mapping algorithms is defined. These algorithms combine topology representing networks and different nonlinear m...
Article
Full-text available
In practical data mining problems high-dimensional data has to be analyzed. In most of these cases it is very informative to map and visualize the hidden structure of complex data set in a low-dimensional space. The aim of this paper is to propose a new mapping algorithm based both on the topology and the metric of the data. The utilized Topology R...
Conference Paper
Full-text available
Difierent clustering algorithms are based on difierent similar- ity or distance measures (e.g. Euclidian distance, Minkowsky distance, Jackard coe-cient, etc.). Jarvis-Patrick clustering method utilizes the number of the common neighbors of the k-nearest neighbors of objects to disclose the clusters. The main drawback of this algorithm is that its...
Conference Paper
Full-text available
As data analysis tasks often have to face the analysis of huge and complex data sets there is a need for new algorithms that combine vector quantization and mapping methods to visualize the hidden data structure in a low-dimensional vector space. In this paper a new class of algorithms is defined. Topology representing networks are applied to quant...
Conference Paper
Full-text available
As data analysis tasks often have to face the analysis of huge and complex data sets there is a need for new algorithms that combine vector quantization and mapping methods to visualize the hidden data structure in a low-dimensional vector space. In this paper a new class of algorithms is defined. Topology representing networks are applied to quant...
Conference Paper
Full-text available
Clustering is an important tool to explore the hidden struc- ture of large databases. There are several algorithms based on difierent approaches (hierarchical, partitional, density-based, model-based, etc.). Most of these algorithms have some discrepancies, e.g. they are not able to detect clusters with convex shapes, the number of the clusters sho...
Article
Full-text available
High-level conceptual database design is a widespread method in database building. With conceptual models we can illustrate the “mini world” of the database in a DBMS-independent form, then with the mapping method we can reach a DBMS-specific model. The database designer should keep in mind both data and functional requirements throughout the whole...

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Projects

Projects (7)
Project
The aim of the project is to develop neural network-based prediction applications and methods.
Project
This project aims to migrate data stored in different database systems.