Melissa Wittens

During her doctoral research, Mandy ‘Melissa’ Jane investigated the pathophysiology of Alzheimer’s disease at the Department of Clinical Neurology in UZ Brussels. Her focus has been on using imaging techniques and fluid biomarkers to improve the early diagnosis and prognosis thereof. Additionally, she contributed to the development of a pre-analytical and mass spectrometry protocol intended for differentiating between various subtypes of cardiac amyloidosis, aiming towards implementation in clinical practice.Prior to her doctoral research, she pursued a master’s degree in biomedical sciences at the University of Antwerp. During this period, her research centered on the isolation and characterization of exosomes for proteomics applications, utilizing innovative differential (ultra)-centrifugation techniques. She also gained valuable experience in clinical trials and statistics through her work as a laboratory scientist at Janssen Pharmaceutica and internships at the Biomedical Informatics Research Network Antwerp. 

Mandy ‘Melissa’ Jane focuses on enhancing trustworthiness and transparency in the secondary use of data within health data spaces. Recognizing the critical role that data quality and integrity play in patient care and clinical decision-making, her work aims to implement measures that align with ethical and legal requirements, promoting data interoperability, discoverability, and accessibility, with the ultimate goal being the facilitation of secure and high-quality data sharing.

She is  investigating key challenges and best practices associated with intra- and inter-organisational data governance in healthcare settings, and how these impact data sharing and collaboration. Furthermore, she is defining essential components of trustworthy clinical decision support systems (CDSS), and how these systems be evaluated to ensure their efficacy and safety in real-world clinical settings. Finally, she is exploring how Human in The Loop (HITL) approaches can enhance the interpretability and trustworthiness of AI in different settings.