Recent advances in the field of data science offer new possibilities in the medical and healthcare sector by enhancing outcome prediction and treatment selection in the clinic. However, the need for interpretable high performance models requires improvement of existing approaches. Typical characteristics of healthcare data include high dimensionality along with low numbers of patients, leading to an increased risk of overfitting models. Adequate feature selection techniques are therefore necessary to restrict the dataset to the most informative columns, resulting in reduced complexity of machine learning models and enhanced data interpretability.
Anna Jenul holds a master’s degree in mathematics from the University of Klagenfurt and currently pursues a PhD degree in data science at the Norwegian University of Life Sciences (NMBU). In her research, she develops machine learning algorithms and statistical methods that focus on analysis of healthcare data such as treatment outcome prediction of cancer patients. Her research interests include feature selection, multiblock methods, as well as Bayesian and computational statistics.
(Image / Video (c) Thomas van Emmerik)
WiDS Villach 2021talk „ Data science for treatment outcome prediction: towards interpretable models combining healthcare data from multiple sources“
