Domain-guided Machine Learning for Healthcare

Professor Yogatheesan Varatharajah at ECE 2026 spring colloquium

Recent advances in wearables, brain implants, and sensing technology have enabled us to design systems that continuously monitor patients' brain health and ascertain individualized treatments for neurological diseases. However, there is a lack of efficient methods that translate continuous physiological data streams into meaningful biological models of underlying diseases, relate them to existing clinical knowledge and biomarkers, and provide actionable treatment parameters. Machine learning (ML) holds great promise in tackling these challenges; however, the mainstream black-box-ML approaches have proven to be untrustworthy because of label inconsistencies, spurious correlations, and the lack of deployment robustness. My goal is to ensure trustworthiness in ML for healthcare, particularly neurology, via a novel framework known as “Domain-guided Machine Learning” or “DGML” that merges machine learning with clinical domain expertise. In this talk, I will discuss the need for trustworthy ML in healthcare, how to leverage clinical domain knowledge to engineer trustworthy ML models, and several real-world applications of DGML in neurological care and decision making.