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Personalized Treatment of Respiratory Diseases Using Artificial Intelligence and Interoperability with e-Health Systems
Myška, Vojtěch ; Drotár,, Peter (referee) ; Brezany, Peter (referee) ; Burget, Radim (advisor)
Corticosteroid (CS) treatment in patients with Long COVID aims to prevent the progression from active post-inflammatory changes to fibrosis scarring. However, CS have side effects, which may sometimes be severe. Some patients might not require any treatment as their post-inflammatory changes resolve spontaneously. This dissertation thesis aims to develop an artificial intelligence (AI) based approach that allows personalized treatment of patients with Long COVID and a design of modular architecture allowing seamless interoperability of AI models with the information systems used in healthcare facilities. The first part of the thesis deals with the foundation of the state-of-the-art of using AI algorithms to recommend CS treatment in patients with Long COVID, who have the risk of permanent lung damage. This study examines how various parameters from different examinations influence the accuracy of the AI models. The most effective model achieves an accuracy of 73.68 %, a balanced accuracy of 73.52 %, and an AUC of 0.7469. These results prove that a trained AI model on a correctly chosen set of parameters from various medical examinations is effective and can be used as a decision-support tool for further treatment courses. The second part focuses on developing a modular architecture that allows interoperability between AI models and the information system of health facilities. Its specific implementation for early COVID-19 detection, incorporating DeepCovidXR models, is presented. In the performance test, the average processing time of X-ray images is 11.53 seconds using the CPU and 2.78 seconds with the GPU. Both values meet the maximum permissible analysis time set at 20 seconds. The results presented in both sections have been implemented and are currently used at the Olomouc University Hospital.
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Modelling and Analysis of Logistics Processes by Applying Process and Data Mining Techniques
Rudnitckaia, Julia ; Wang, Hao (referee) ; Zendulka, Jaroslav (referee) ; Hruška, Tomáš (advisor)
In this thesis, we propose an approach for modelling hidden and unknown processes and subprocesses in the example of a seaport logistics area. Having the underlying process model makes it possible to exploit more advanced algorithms since deviations and main paths are becoming visible and better controlled. The obtained model is the foundation for the core research of this work and will be enriched with key performing indicators and their forecast by applying advanced process mining, statistics, and machine learning techniques. The main difference of the approach is that we take as a target variable not any specific value, but the object - a process variant or a process type with a set of parameters. Bottleneck analysis, from one side, and predictive analysis, on the other hand, are enforced with context-aware information, especially with these additional objective process attributes. Furthermore, the support of the descriptive ("As is") current process model with certain notation and the integration with relevant bottleneck and predictive methods compromise the advantages of the approach. The work primarily focuses on the design of algorithms and methods for supporting logistics data analysis. However, it can be adjusted and applied to other areas accordingly, which makes the approach flexible and versatile. The result of the work is the framework for unstructured process modelling and the key process parameters predictive method. This analysis of processes with their attributes might be used for decision-making systems and process maps in future.
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Analysis of retinal nerve fiber layer for diagnosis of glaucoma
Vodáková, Martina ; Malínský, Miloš (referee) ; Odstrčilík, Jan (advisor)
The master thesis is focused on creating a methodology for quantification of the nerve fiber layer on photographs of the retina. The introductory part of the text presents a medical motivation of the thesis and mentions several studies dealing with this issue. Furthermore, the work describes available textural features and compares their ability to quantify the thickness of the nerve fiber layer. Based on the described knowledge, the methodology to make different regression models enabling prediction of the retinal nerve fiber layer thickness was developed. Then, the methodology was tested on the available image dataset. The results showed, that the outputs of regression models achieve a high correlation between the predicted output and the retinal nerve fiber layer thickness measured by optical coherence tomography. The conclusion discusses an usability of the applied solution.
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Rating of Financial Health of the Selected Company
Žáček, Zdeněk ; Směřička, Marek (referee) ; Zemánková, Lenka (advisor)
Bachelor´s thesis is concerned with rating of financial health of the company Energetické strojírny Brno, a. s. through financial analysis. On the collected theoretical basis of financial analysis is worked up financial analysis of the selected company. Resulting rates are compared with occupational avarages or recommended rates and objectively reviewed. In the case of negative results are identified their causes. At the conclusion are proposed acquisitions for improvement of financial and economic situation of the company.
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The Business Plan of the Porterhouse BeerPub
Švanda, Martin ; Trlida, Martin (referee) ; Luňáček, Jiří (advisor)
The diploma thesis focuses on creating a real budiness plan for establishment a porter house, that will offer beer from production of small breweries. Part of the thesis is also a making of prediction models for sales and costs in the branch. It also contains a financial plan and evaluation of the investment effectivity.
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Modelling and Analysis of Logistics Processes by Applying Process and Data Mining Techniques
Rudnitckaia, Julia ; Wang, Hao (referee) ; Zendulka, Jaroslav (referee) ; Hruška, Tomáš (advisor)
In this thesis, we propose an approach for modelling hidden and unknown processes and subprocesses in the example of a seaport logistics area. Having the underlying process model makes it possible to exploit more advanced algorithms since deviations and main paths are becoming visible and better controlled. The obtained model is the foundation for the core research of this work and will be enriched with key performing indicators and their forecast by applying advanced process mining, statistics, and machine learning techniques. The main difference of the approach is that we take as a target variable not any specific value, but the object - a process variant or a process type with a set of parameters. Bottleneck analysis, from one side, and predictive analysis, on the other hand, are enforced with context-aware information, especially with these additional objective process attributes. Furthermore, the support of the descriptive ("As is") current process model with certain notation and the integration with relevant bottleneck and predictive methods compromise the advantages of the approach. The work primarily focuses on the design of algorithms and methods for supporting logistics data analysis. However, it can be adjusted and applied to other areas accordingly, which makes the approach flexible and versatile. The result of the work is the framework for unstructured process modelling and the key process parameters predictive method. This analysis of processes with their attributes might be used for decision-making systems and process maps in future.
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Evaluation of the financial situation of the company using systems of financial indicators
HRBOVÁ, Veronika
The aim of this thesis is to evaluate the financial situation of the company using parallel, pyramid and purpose systems of financial indicators. The selected company of this thesis is the company Kores Europe s.r.o., which deals mainly with the production of glue sticks. The goal was achieved through various methods of financial analysis - a parallel set of indicators, a pyramid set of indicators, purposely selected set of financial indicators and an intercompany comparison. The analysed period is from 2016 to 2020. The results are interpreted and compared with a sample of companies from the Bisnode Albertina database. Finally, potential sources of inefficient finance and recommendations for improving the financial situation of the company are identified.
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Financial analysis of the company
KAPLANOVÁ, Lucie
The objective of this work is to evaluate the development of the financial situation of a selected company, to identify possible sources of inefficiency and to propose measures to eliminate them. The theoretical part is focused on the main objectives, subject and methods financial analysis. The practical part deals with the analysis of the selected company based on financial statements using horizontal and vertical analyses, an analysis of differences and ratios, the breakdown of return on equity, predictive and diagnostic models. The values of selected indicators are further compared with companies from the same industry.
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The financial analysis of DEVELOP MOST s.r.o.
Nyklová, Anna ; Sieber, Martina (advisor) ; Loužek, Marek (referee)
The bachelor thesis will deal with the financial analysis of the company DEVELOP MOST s.r.o. The thesis is divided into a theoretical and a practical part. In the theoretical part, there is an explanation of the basic concept and the methods and ratios used in the financial analysis. In the practical part, individual ratios will be applied to the selected company. The aim was to reveal the strengths and weaknesses of the company. In addition, the thesis will contain a segmentation analysis of the market in which the company operates as well as an evaluation and comparison of the main competitors.
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