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Found 6 papers, 5 papers with code
EHRFL: Federated Learning Framework for Heterogeneous EHRs and Precision-guided Selection of Participating Clients
In this study, we provide solutions to two practical yet overlooked scenarios in federated learning for electronic health records (EHRs): firstly, we introduce EHRFL, a framework that facilitates federated learning across healthcare institutions with distinct medical coding systems and database schemas using text-based linearization of EHRs.
General-Purpose Retrieval-Enhanced Medical Prediction Model Using Near-Infinite History
This approach effectively eliminates the need for manual feature selection and enables an unrestricted observation window.
Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes
The development of large language models tailored for handling patients' clinical notes is often hindered by the limited accessibility and usability of these notes due to strict privacy regulations.
UniHPF : Universal Healthcare Predictive Framework with Zero Domain Knowledge
Despite the abundance of Electronic Healthcare Records (EHR), its heterogeneity restricts the utilization of medical data in building predictive models.
Universal EHR Federated Learning Framework
Federated learning (FL) is the most practical multi-source learning method for electronic healthcare records (EHR).
GenHPF: General Healthcare Predictive Framework with Multi-task Multi-source Learning
To address this challenge, we propose General Healthcare Predictive Framework (GenHPF), which is applicable to any EHR with minimal preprocessing for multiple prediction tasks.
