论文标题
UNIHPF：具有零领域知识的通用医疗预测框架
UniHPF : Universal Healthcare Predictive Framework with Zero Domain Knowledge
论文作者
论文摘要
尽管电子保健记录（EHR）丰富，但其异质性限制了医疗数据在构建预测模型中的利用。为了应对这一挑战，我们提出了通用的医疗预测框架（UNIHPF），该框架不需要医疗领域知识和对多个预测任务的最小预处理。实验结果表明，UNIHPF能够构建大规模的EHR模型，这些模型可以从不同的EHR系统处理任何形式的医疗数据。我们认为，我们的发现可以提供有用的见解，以进一步研究EHR的多源学习。
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.
