The modelling of Electronic Health Records (EHRs) has the potential to drive more efficient allocation of healthcare resources, enabling early intervention strategies and advancing personalised healthcare. However, EHRs are challenging to model due to their realisation as noisy, multi-modal data occurring at irregular time intervals. To address their temporal nature, we treat EHRs as samples generated by a Temporal Point Process (TPP), enabling us to model what happened in an event with when it happened in a principled way. We gather and propose neural network parameterisations of TPPs, collectively referred to as Neural TPPs. We perform evaluations on synthetic EHRs as well as on a set of established benchmarks. We show that TPPs significantly outperform their non-TPP counterparts on EHRs. We also show that an assumption of many Neural TPPs, that the class distribution is conditionally independent of time, reduces performance on EHRs. Finally, our proposed attention-based Neural TPP performs favourably compared to existing models, and provides insight into how it models the EHR, an important step towards a component of clinical decision support systems.

研究了医疗资源分配的效率和定制化医疗的发展，使用Temporal Point Process（TPP）来建模EHRs，提出了神经网络参数化的TPPs，经过对合成EHRs和基准数据的评估，表明TPPs在EHRs上明显优于非TPP同行，提出了注意力机制神经TPP，优于现有模型，符合现实世界的可解释性要求，是临床决策支持系统的重要组成部分。

神经时序点过程模型用于电子健康记录建模