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Computer-aided data quality monitoring and assessment in clinical research
Šiška, Branislav ; Kolářová, Jana (referee) ; Schwarz, Daniel (advisor)
The diploma thesis deals with the monitoring and evaluation of data in clinical research. Usual methods to identify incorrect data are one-dimensional statistical methods per each variable in the register. Proposed method enters directly into database and finds out outliers in data using machine learning combined with multidimensional statistical methods that transform all column variables of clinical register to one, representing one record of patient in the register. Algorithm of proposed method is written in Matlab.
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Algorithms for anomaly detection in data from clinical trials and health registries
Bondarenko, Maxim ; Blaha, Milan (referee) ; Schwarz, Daniel (advisor)
This master's thesis deals with the problems of anomalies detection in data from clinical trials and medical registries. The purpose of this work is to perform literary research about quality of data in clinical trials and to design a personal algorithm for detection of anomalous records based on machine learning methods in real clinical data from current or completed clinical trials or medical registries. In the practical part is described the implemented algorithm of detection, consists of several parts: import of data from information system, preprocessing and transformation of imported data records with variables of different data types into numerical vectors, using well known statistical methods for detection outliers and evaluation of the quality and accuracy of the algorithm. The result of creating the algorithm is vector of parameters containing anomalies, which has to make the work of data manager easier. This algorithm is designed for extension the palette of information system functions (CLADE-IS) on automatic monitoring the quality of data by detecting anomalous records.
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Algorithms for anomaly detection in data from clinical trials and health registries
Bondarenko, Maxim ; Blaha, Milan (referee) ; Schwarz, Daniel (advisor)
This master's thesis deals with the problems of anomalies detection in data from clinical trials and medical registries. The purpose of this work is to perform literary research about quality of data in clinical trials and to design a personal algorithm for detection of anomalous records based on machine learning methods in real clinical data from current or completed clinical trials or medical registries. In the practical part is described the implemented algorithm of detection, consists of several parts: import of data from information system, preprocessing and transformation of imported data records with variables of different data types into numerical vectors, using well known statistical methods for detection outliers and evaluation of the quality and accuracy of the algorithm. The result of creating the algorithm is vector of parameters containing anomalies, which has to make the work of data manager easier. This algorithm is designed for extension the palette of information system functions (CLADE-IS) on automatic monitoring the quality of data by detecting anomalous records.
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Selected impacts of missing data problem in economics
Uenal, Hatice
Data sources and data quality are indispensable in economical, medical, pharmaceutical or other studies and provide the basis for reliable study results in numerous research questions. Depending on the purpose of use, a high quality of data is a prerequisite. However, with increasing registry quality, costs also increase accordingly. Considering these time and cost consuming factors, this work is an attempt to estimate the cost advantages when applying statistical tools to existing registry data. This includes methodological considerations and suggestions regarding the evaluation of data quality including factors such as bias and reliability after dealing properly (or not) with missing data (MD), and possible consequences when ignoring the incompleteness of data. Results for the quality analysis of the gastric cancer patients’ data example showed that millions of Euros in study costs can be saved by reducing the time horizon. On average, €523,126.70 can be saved for every year that the study duration is shortened. Replacing additionally the over 25% of MD in some variables, data quality was immensely improved, but still showed quality difficulties, which – beside MD in variables – could be an indication for completely missing entries of patients in the registry. Capturerecapture methods were therefore discussed to demonstrate how the total completeness in a registry can be estimated. Since it was not possible to illustrate the CARE method with the example of the gastric cancer patients due to the given data structure (no access to required variables), other data sets had to be chosen – the publicly accessible data of the amyotrophic lateral sclerosis (ALS) and data of towed vehicles in the City of Chicago. The consequence of ignoring MD was further analyzed using bankruptcy prediction data sets of agribusiness companies and confirmed the assumption that MD have a negative impact on the data quality, in this case also regarding the misclassifications of predictions of bankrupted companies. Using the decision tree method (known as one of the most suitable methods in predicting financial distress), the percentage of correctly bankruptcy-predicted of bankrupted companies (one year to bankruptcy) with MD imputation was 87.5%, whereas it was only 60% when completely omitting MD. Overall, my findings showed dearly the importance of statistical methods to improve data quality which in turn helps to avoid drawing biased conclusions due to incomplete data.
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Measurement of (anti)immigration Attitudes from the Methodological Perspective. Quality of Measurement with the Special Focus on Measurement Equivalence
Šarapatková, Anna ; Remr, Jiří (advisor) ; Soukup, Petr (referee)
Opportunities that we have in today's world are sharply evolving, and the world is changing all together with these changes. This development is noticeably observed within the topic of global movement of (not only) population, which has changed fundamentally, both economically, politically and socially. Today's so much diversified form of migration, which has lost its transparency it used to has, is a very up to date and debated topic currently almost all over the world. Because of high importance of the topic "migration" it is often subject of research and number of surveys. One of the most examined area within the topic migration is attitudes of people towards immigration and immigrant, oftentimes together with investigating cause leading to particular attitude. Due to the international reach of the topic, these attitudes are often subject of cross-national research or national research, which, however, use data from international surveys. There is a clear disparity across European states in these attitudes towards immigration and, above all, the immigrants themselves. Given this nature of cross-national surveys measuring attitudes towards immigrants, it is important to focus on the measurement quality, which is becoming increasingly complex in the perspective of international research. It is...
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Algorithms for anomaly detection in data from clinical trials and health registries
Bondarenko, Maxim ; Blaha, Milan (referee) ; Schwarz, Daniel (advisor)
This master's thesis deals with the problems of anomalies detection in data from clinical trials and medical registries. The purpose of this work is to perform literary research about quality of data in clinical trials and to design a personal algorithm for detection of anomalous records based on machine learning methods in real clinical data from current or completed clinical trials or medical registries. In the practical part is described the implemented algorithm of detection, consists of several parts: import of data from information system, preprocessing and transformation of imported data records with variables of different data types into numerical vectors, using well known statistical methods for detection outliers and evaluation of the quality and accuracy of the algorithm. The result of creating the algorithm is vector of parameters containing anomalies, which has to make the work of data manager easier. This algorithm is designed for extension the palette of information system functions (CLADE-IS) on automatic monitoring the quality of data by detecting anomalous records.
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Algorithms for anomaly detection in data from clinical trials and health registries
Bondarenko, Maxim ; Blaha, Milan (referee) ; Schwarz, Daniel (advisor)
This master's thesis deals with the problems of anomalies detection in data from clinical trials and medical registries. The purpose of this work is to perform literary research about quality of data in clinical trials and to design a personal algorithm for detection of anomalous records based on machine learning methods in real clinical data from current or completed clinical trials or medical registries. In the practical part is described the implemented algorithm of detection, consists of several parts: import of data from information system, preprocessing and transformation of imported data records with variables of different data types into numerical vectors, using well known statistical methods for detection outliers and evaluation of the quality and accuracy of the algorithm. The result of creating the algorithm is vector of parameters containing anomalies, which has to make the work of data manager easier. This algorithm is designed for extension the palette of information system functions (CLADE-IS) on automatic monitoring the quality of data by detecting anomalous records.
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Computer-aided data quality monitoring and assessment in clinical research
Šiška, Branislav ; Kolářová, Jana (referee) ; Schwarz, Daniel (advisor)
The diploma thesis deals with the monitoring and evaluation of data in clinical research. Usual methods to identify incorrect data are one-dimensional statistical methods per each variable in the register. Proposed method enters directly into database and finds out outliers in data using machine learning combined with multidimensional statistical methods that transform all column variables of clinical register to one, representing one record of patient in the register. Algorithm of proposed method is written in Matlab.
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