NIH R01 · 2024
Clinical foundation model for structured clinical data
Abstract In the era of big clinical data, the availability of rich real-world clinical data sources (RWcD) enables the development of predictive models for different clinical events, bringing the potential to improve efficiency and lower the cost of health care. However, the currently in-use models in practice are mostly trained on local data, introducing issues of bias and lack of generalizability. We will develop comprehensive methods to efficiently train high-quality clinical foundation model (CFM) that learn informative representations from patients' structured clinical data either in the form of EHR or claims. Specifically, how to train CFM that can maximize the performance boost for…
From the public funding record at NIH RePORTER. Describes the funded project, not the reviews below.