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Found 6 papers, 5 papers with code
Rediscovery of CNN's Versatility for Text-based Encoding of Raw Electronic Health Records
Making the most use of abundant information in electronic health records (EHR) is rapidly becoming an important topic in the medical domain.
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.
Unifying Heterogeneous Electronic Health Records Systems via Text-Based Code Embedding
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.
Unifying Heterogeneous Electronic Health Records Systems via Text-Based Code Embedding
To overcome this problem, we introduce Description-based Embedding, DescEmb, a code-agnostic description-based representation learning framework for predictive modeling on EHR.
