Pregled bibliografske jedinice broj: 1079350
Exploratory Analysis of the Most Frequent Chronic and Acute Diagnoses Before the Onset of Coronavirus: A Case Study from Croatia
Exploratory Analysis of the Most Frequent Chronic and Acute Diagnoses Before the Onset of Coronavirus: A Case Study from Croatia, 2020., diplomski rad, preddiplomski, Fakultet elektrotehnike i računarstva, Zagreb
CROSBI ID: 1079350 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Exploratory Analysis of the Most Frequent Chronic
and Acute Diagnoses Before the Onset of
Coronavirus: A Case Study from Croatia
Autori
Dominik Bezić
Vrsta, podvrsta i kategorija rada
Ocjenski radovi, diplomski rad, preddiplomski
Fakultet
Fakultet elektrotehnike i računarstva
Mjesto
Zagreb
Datum
13.07
Godina
2020
Stranica
53
Mentor
Bagić Babac, Marina
Ključne riječi
health ; chronic diseases ; acute diseases ; explanatory analysis
Sažetak
This thesis aims to explore the most frequent chronic and acute diagnoses for patients in Croatia based on a large anonymized dataset of medical prescriptions during the period from 2017 until 2019. A statistical analysis of a dataset is based on the International Classification of Diseases. A set of categorical and numerical variables is chosen to provide an in-depth overview of the distribution of chronic and acute diagnoses and to allow for comparison across Croatian counties and over the time to identify trends. Firstly, two large datasets are collected with over 500, 000 records combined. Each of the datasets consists of different diagnoses for various groups of people. Then, the data is grouped for more specific analysis based on a variety of attributes (e.g. sex, age group, or counties). Finally, an explanatory analysis is conducted to discover patterns, check assumptions, and spot correlations between properties with the help of summary statistics and graphical representations. Despite the limitation of this study that it relies on data acquired before the onset of coronavirus in the world as well as in Croatia, a predictive model is proposed to anticipate future trends in state of the health of the nation.
Izvorni jezik
Engleski
Znanstvena područja
Računarstvo, Interdisciplinarne tehničke znanosti, Javno zdravstvo i zdravstvena zaštita, Informacijske i komunikacijske znanosti
POVEZANOST RADA
Ustanove:
Fakultet elektrotehnike i računarstva, Zagreb
Profili:
Marina Bagić Babac
(mentor)