RESEARCH OVERVIEW

I am researching ways to ensure that machine learning models are robust to data distribution changes after deployment in healthcare.

Patient information recorded in electronic health records (EHR) are recorded as asynchronous events. For machine learning models, it is often convenient to convert these representations into synchronous blocks of time. This transformation results in discretised data with exceptionally high amounts of missing data (i.e. blood samples are not drawn hourly). I am interested in ways to represent patient trajectories as a single vector in a robust manner so that we can fine tune models for automated clinical decisions, ensure performance when transporting models across multiple institutes, and create decision performance that endures the chaotic evolution of medicine.

For a list of my publications, please check my Google Scholar page.