07-26, 09:30–10:00 (US/Eastern), Online talks and posters
Working with OMOP CDM involves managing large datasets, requiring efficient data extraction tools. To enhance JuliaHealth's infrastructure, we'll expand tools and enable diverse database connections. This aids in developing a patient-level prediction framework for cohort outcomes, tested on mimic and real patient data. The poster is influenced by a Google Summer of Code project, sharing aspects of this journey.
Working with the OMOP CDM (Observational Medical Outcomes Partnership Common Data Model) involves handling large datasets that require a set of tools for extracting the necessary data efficiently. The first part of the project focuses on improving JuliaHealth's infrastructure by increasing the range of tools available to users. This involves enabling connections to various databases, and working with observational health data. Our second goal is to leverage the capacity built in the previous phase to develop a comprehensive framework for patient-level prediction. This framework will predict patient cohort outcomes with given treatments and will be tested on mimic data, and potentially on real aggregated and anonymized patient data.
