  Despite the abundance of Electronic Healthcare Records (EHR), its
heterogeneity restricts the utilization of medical data in building predictive
models. To address this challenge, we propose Universal Healthcare Predictive
Framework (UniHPF), which requires no medical domain knowledge and minimal
pre-processing for multiple prediction tasks. Experimental results demonstrate
that UniHPF is capable of building large-scale EHR models that can process any
form of medical data from distinct EHR systems. We believe that our findings
can provide helpful insights for further research on the multi-source learning
of EHRs.
