Thesis supervisor: Attila Csaba Nagy
Location of studies (in Hungarian): Debreceni Egyetem Informatikai Kar Abbreviation of location of studies: ITDI
Description of the research topic:
The aim of the research is to convert the large amount of cardiometabolic data, generated during clinical data collection, into an analysable format. It also aims to combine and narrow down different data tables through automatic screening criteria. Extraction of valuable information from free text fields that are not available elsewhere, by text mining methods is a key aim too. Besides descriptive statistics, we also want to explore associations in a cleaned dataset using machine learning in addition to traditional statistical methods.
Bibliography
1. International Diabetes Federation. IDF Diabetes Atlas, 9th edn. Brussels, Belgium: International Diabetes Federation, 2019.
2. H Dalianis: Clinical Text Mining, Springer, 2018.
3. JPB Barton: Medical Statistics. John Wiley and Sons, 2014.