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, whilst aligning with real world
interpretability requirements, 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

Neural Temporal Point Processes For Modelling Electronic Health Records

The increased use of electronic health records has made possible the
automated extraction of medical policies from patient records to aid in the
development of clinical decision support systems. We adapted a boosted
Statistical Relational Learning (SRL) framework to learn probabilistic rules
from clinical hospital records for the management of physiologic parameters of
children with severe cardiac or respiratory failure who were managed with
extracorporeal membrane oxygenation. In this preliminary study, the results
were promising. In particular, the algorithm returned logic rules for medical
actions that are consistent with medical reasoning.

本文介绍一种利用医疗记录自动提取医疗策略的机器学习方法，并针对百科膜肺氧合技术治疗严重心脏或呼吸衰竭的儿童的生理参数进行实验，结果发现该算法的逻辑规则与医学推理一致。