Ghazaleh Niknam
Ghazaleh is a computational health researcher with expertise in machine learning, graph modelling, and clinical data analysis. She completed a PhD in dynamic graph representation learning and held a visiting fellowship at the Computational Health Informatics Lab at the University of Oxford. Now a Postdoctoral Fellow at UNSW and the Ingham Institute, she works with the SPHERE Cancer Group to study variation in cancer care using OMOP-harmonised data, federated analytics, and privacy-preserving tools.
Session
Clinical data harmonisation efforts are an extraordinarily powerful tool in the world of observational research. When your data model is designed to do everything, however, there is a necessary trade-off in design principles. The requirement to support every possible use-case across all clinical domains means that they can tend to favour flexibility over clarity, storing events, measurements, treatments, and outcomes in highly normalised, loosely typed schemas. For domain experts like oncology researchers or clinicians, this makes even basic questions (say, “what happened to this patient, when, and why?”) frustratingly opaque.
Using Python’s ORM paradigm, we created a more intuitive, opinionated view of oncology data. By surfacing richly connected objects like CancerPatient, CancerDiagnosis, Regimen, or Cycle, we move away from brittle SQL scripts and toward a model that reflects how clinical experts already think. These ORM-backed tools not only support reproducible ETL and visualisation workflows, but also allow non-developers to explore complex patient journeys in a hands-on, object-based way. We’re building out a library of reusable object maps that encode domain knowledge directly, letting researchers focus on clinical questions and not worry about the nuanced query logic.