Team Member

Noman is a postdoctoral researcher at Amsterdam UMC, residing at the intersection of artificial intelligence (AI) and cardiology. He holds a PhD in Medical Informatics, with his doctoral research focusing on using machine learning and Natural Language Processing (NLP) to predict falls in older adults through the analysis of electronic health records.

Currently, Noman leverages generative AI and NLP, particularly through the use of Large Language Models (LLMs), to extract valuable insights from narrative text within Electronic Health Records (EHRs). His work aims to empower clinicians with evidence-based recommendations that align with the guidelines established by the European Society of Cardiology (ESC). He focuses on developing cardiovascular risk prediction models and automating information extraction processes. His work forms an integral part of the DataTools4Heart project, aimed at developing a comprehensive cardiology data toolbox to promote collaboration among clinicians, researchers, and data scientists across institutions.