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Monitorovanie, hodnotenie a výskum efektívnosti alkoholovej liečby závislosti na medzinárodnej úrovni, s európskou pridanou hodnotou / Monitoring, Evaluation and Research on Effectiveness of Alkohol Treatment on an International Level with European Added ValueStanislav, Vladimír January 2017 (has links)
Background: Alcohol use disorders belong among the ten leading causes of Years Lost due to Disability in high-income countries. Poland, Czech Republic, and the Slovak Republic are countries with high alcohol consumption. The specific inpatient psychotherapeutic program is basic treatment approach in patients suffering from alcohol dependency. The theory of change assumes that therapeutic approaches should be adapted to the stage of change in which the patient actually is. Aim: To examine the state of readiness to change at the beginning and the end of inpatient short (six weeks) and long (12 weeks) therapeutic program in Slovak Republic, Poland, and the Czech Republic. To compare readiness to change with insight and motivation. To find, whether patients change and how patients change advances in alcohol treatment. Methods: Total 380 alcohol dependent inpatients (282 men and 98 women) were examined using International Statistical Classification of Diseases and Related Health Problems- 10th Revision (ICD- 10), World Health Organization (1992). Alcohol Use Disorders Identification Test (AUDIT), The Stages of Change Readiness and Treatment Eagerness Scale (SOCRATES), Readiness to Change Questionnaire (RCQ), and Demographic Questionnaire. All analyses were calculated using the SPSS (Statistical Packages...
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Počítačová podpora pro monitoring a hodnocení kvality dat v klinickém výzkumu / Computer-aided data quality monitoring and assessment in clinical researchŠiška, Branislav January 2018 (has links)
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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