Disease Comorbidity Analysis
Name
Ants-Oskar Mäesalu
Abstract
Abstract:
Personalised medicine is a new approach to health care, in which the focus is on the individuality of patients, and disease prediction and prevention are emphasised, as opposed to only reacting to the consequences of medical disorders. As much data about the patients and diseases as possible, as well as other medical information, is taken into account while attempting to find if and how they are linked to each other. The main objective of personalised medicine is to offer more effective treatment to every patient in a shorter period of time at a lower cost in the future.
The aim of this thesis is to study and analyse disease comorbidity in the Estonian population. 2x2 contingency tables are constructed about every pair of co-occurring ICD-10 diagnose codes in epicrises gathered by the Estonian E-Health Foundation in the years 2012-2013. The potential correlation between diseases is measured with Fisher’s exact test and diagnose pairs with a stronger association are filtered. The results are visualised using heat maps. Disease comorbidity analysis is a prerequisite for future research about disease episode mining.
Keywords:
bioinformatics, personalised medicine, epidemiology, disease comorbidity, ICD 10, 2x2 contingency tables, Fisher’s exact test
Graduation Thesis language
English
Graduation Thesis type
Bachelor - Computer Science
Supervisor(s)
Jaak Vilo
Defence year
2015