Object-Oriented Oncology: Making Sense of Complex Patient Journeys
summary
Claims a concrete implementation: a Python ORM that maps OMOP plus the oncology extension into rich CancerPatient/CancerDiagnosis objects and exposes episodes and modifiers so clinical timelines, regimens and cycles are first‑class. ETL becomes reproducible and testable, visualisations pull nested episode events in one call, and a beta library exists. Performance needs matter — currently replacing six‑monthly manual reports with weekly updates is realistic but requires materialised views and optimisation.
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chapters (10)
- 0:00Object-Oriented Oncology intro
- 1:25Three healthcare research stories
- 5:18Open science communities
- 7:01Federated networks with data models
- 10:53Oncology data complexity challenges
- 13:03General vs oncology-specific models
- 15:59Research translation into care
- 18:04Python objects and ORM
- 23:20Clinical data visualization
- 24:03Q&A
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