Detection of Mislabeled Blood Samples in Clinical Settings using Machine Learning

Baris Gün Sürmeli
Research Theme
Application area: Digital Health R1 Human agency
Machine Learning

The focus of this PhD thesis centers on the development and application of machine learning methodologies for the precise identification of mislabeled blood samples in clinical settings. Leveraging laboratory test results as discriminative features, the research aims to enhance the accuracy and reliability of blood sample labeling, thereby augmenting patient safety and healthcare quality.