1 code implementation • 20 Apr 2024 • Jiyoun Kim, Junu Kim, Kyunghoon Hur, Edward Choi
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.
1 code implementation • 15 Mar 2023 • Eunbyeol Cho, Min Jae Lee, Kyunghoon Hur, Jiyoun Kim, Jinsung Yoon, Edward Choi
Making the most use of abundant information in electronic health records (EHR) is rapidly becoming an important topic in the medical domain.
no code implementations • 15 Nov 2022 • Kyunghoon Hur, JungWoo Oh, Junu Kim, Jiyoun Kim, Min Jae Lee, Eunbyeol Cho, Seong-Eun Moon, Young-Hak Kim, Edward Choi
Despite the abundance of Electronic Healthcare Records (EHR), its heterogeneity restricts the utilization of medical data in building predictive models.
1 code implementation • 14 Nov 2022 • Junu Kim, Kyunghoon Hur, Seongjun Yang, Edward Choi
Federated learning (FL) is the most practical multi-source learning method for electronic healthcare records (EHR).
2 code implementations • 20 Jul 2022 • Kyunghoon Hur, JungWoo Oh, Junu Kim, Jiyoun Kim, Min Jae Lee, Eunbyeol Cho, Seong-Eun Moon, Young-Hak Kim, Louis Atallah, Edward Choi
To address this challenge, we propose General Healthcare Predictive Framework (GenHPF), which is applicable to any EHR with minimal preprocessing for multiple prediction tasks.
1 code implementation • 12 Nov 2021 • Kyunghoon Hur, Jiyoung Lee, JungWoo Oh, Wesley Price, Young-Hak Kim, Edward Choi
EHR systems lack a unified code system forrepresenting medical concepts, which acts asa barrier for the deployment of deep learningmodels in large scale to multiple clinics and hos-pitals.
1 code implementation • 8 Aug 2021 • Kyunghoon Hur, Jiyoung Lee, JungWoo Oh, Wesley Price, Young-Hak Kim, Edward Choi
To overcome this problem, we introduce Description-based Embedding, DescEmb, a code-agnostic description-based representation learning framework for predictive modeling on EHR.