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The Best PhD Paper Award, announced annually at the dHealth Conference, was awarded in 2025 to Veronika Gomb谩s, research assistant at the Department of Systems and Computer Science of the Faculty of Information Technology, University 蜜糖直播, for her paper titled 鈥淐lassifying Type 2 Diabetes Using N-Glycan Profiling and Machine Learning Algorithms.鈥� Her supervisor is Dr. 脕gnes Vathy Fogarassyn茅, associate professor at the department.

Best PhD Paper Award

The aim of the award is to recognize and encourage outstanding scientific contributions by doctoral students in the fields of health informatics and digital health. Eligible submissions are studies related to a PhD dissertation by candidates currently enrolled in a doctoral program or those who obtained their PhD in the previous year. The award not only acknowledges scientific excellence among young researchers but also supports their integration into the academic community and their contributions to the advancement of healthcare technologies.

dHealth Conference

The Digital Health Conference is an internationally recognized scientific and professional event held annually in Vienna. Its goal is to provide a platform for researchers, healthcare professionals, IT developers, and policymakers to exchange knowledge and foster multidisciplinary collaboration. Topics covered include healthcare data analytics, artificial intelligence in medicine, e-health applications, and patient-centered digital solutions. The dHealth Conference places a strong emphasis on the digital transformation of European healthcare systems while offering opportunities to present the latest research findings and practical innovations.

The paper presented at the conference showcases the first results of a joint research initiative between the Healthcare Analytics Research and Development Center and the Institute of Bio-Nanotechnology and Technical Chemistry. The study confirmed that type 2 diabetes can be identified with high accuracy using machine learning algorithms applied to N-glycan profiles derived from blood serum samples. The research involved identifying N-glycan structures relevant to type 2 diabetes and developing a machine learning-based automatic classification method. The classifier method based on the Extra Trees Classifier produced outstanding results (accuracy: 0.8982, sensitivity: 0.8966, specificity: 0.9000), indicating that the combination of N-glycan profiles from blood serum and machine learning algorithms holds great promise for the early diagnosis of type 2 diabetes.

Our heartfelt congratulations on this well-deserved recognition!