Aims and Objectives
In newborn screening diagnostics blood samples taken from newborns within a few days after birth are analyzed to identify rare metabolic diseases and hormonal disorders. The accurate and efficient diagnosis of these diseases is important but challenging, due to their low prevalence. A quick diagnosis can lead to efficient therapies and treatments which can positively change the outcome and severity of the disease for the children. Thanks to advances in data mining and machine learning as well as the computing landscape in recent years, new opportunities to examine large data sets with high dimensional feature spaces have been developed. In particular, Knowledge Discovery in Databases (KDD) methods can be helpful to discover new clues for unknown causal relations as well as novel metabolic patterns. A classical approach is to consider classification schemes, which rely on mathematical models describing the underlying relationships. When developing these classification models real-world data has to be processed, so the medical input data and the mathematical models are subjected to measurement inaccuracies and uncertainties.
In our project, we closely collaborate with clinicians from the University Clinic Heidelberg to analyze and develop innovative methods for mathematical uncertainty quantification (UQ) to describe and quantify noise in the data, in order to obtain reliable classification results for newborn screening. In that context, it is important not only to ensure the reliability of the model, but also to make the model understandable and interpretable for practitioners. Specifically, we want to extend techniques of Explainable AI (XAI) in the context of rarre disease diagnosis. The overall goal is to analyze and develop reliable and interpretable models with high diagnostic prediction to support the diagnosis of rare diseases within newborns.
Mathematical Modeling for rare disease diasgnosis
Deep Learning / Data Mining
People from EMCL