| dc.description.abstract | The usage of Machine Learning in medicine is a new and a very fast
moving technology which is getting more and more attention by information
technology companies, doctors, patients and scientists. This technology holds
promise for several aspects of medicine, including improving diagnosis of
disease, early detection of disease and personalized health care.
Currently, experiments with real-world clinical data are necessary
to investigate how models based on di erent statistical analysis methods
perform in clinical practice. Previous research has observed and measured the
influence of various predictors on survival after a cardiac arrest event, both
in the form of biomarkers present in the results of blood analysis and from
other types of patient information. This master's thesis project continues
this study, trying to nd better models using di erent advanced modeling
methods for the prediction of several factors related to disease outcome using
a large and comperhensive dataset of clinical data. | |
