Continuous Patient State Attention Models
Continuous Patient State Attention Models
复制标题
连续患者状态注意力模型
DOI：
10.1101/2022.12.23.22283908
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Chauhan V
中科院分区：
文献类型：
--
作者：
Chauhan V
不规则时间序列（ITS）在电子健康记录（EHR）中很普遍，因为数据是根据临床指南/要求记录在EHR系统中的，但不是用于研究，也取决于患者的健康状况。ITS对机器学习算法的训练提出了挑战，这些算法大多建立在一致的固定维特征空间的假设上。在本文中，我们提出了一个计算效率高的变种的Transformer的基础上的想法，交叉注意，称为感知器，时间序列在医疗保健。我们进一步开发了连续病人状态注意模型，使用感知器和Transformer来处理电子病历中的ITS。连续患者状态模型利用神经常微分方程来学习患者健康动态，即，从观察到的不规则时间步长中提取患者健康轨迹，这使得他们能够在任何时间对任何数量的时间步长进行采样。在Physionet-2012挑战和MIMIC-III数据集上，对所提出的模型在医院死亡率预测任务上的性能进行了评估。与Transformer模型相比，Perceiver模型的性能显著优于基线，并降低了计算复杂度，而没有显著的性能损失。精心设计的研究医疗保健不规则性的实验也表明，连续患者状态模型优于基线。该代码在www.example.com上公开发布和验证。
Irregular time-series (ITS) are prevalent in the electronic health records (EHR) as the data is recorded in EHR system as per the clinical guidelines/requirements but not for research and also depends on the patient health status. ITS present challenges in training of machine learning algorithms, which are mostly built on assumption of coherent fixed dimensional feature space. In this paper, we propose a computationally efficient variant of the transformer based on the idea of cross-attention, called Perceiver, for time-series in healthcare. We further develop continuous patient state attention models, using the Perceiver and the transformer to deal with ITS in EHR. The continuous patient state models utilise neural ordinary differential equations to learn the patient health dynamics, i.e., patient health trajectory from the observed irregular time-steps, which enables them to sample any number of time-steps at any time. The performance of the proposed models is evaluated on in-hospital-mortality prediction task on Physionet-2012 challenge and MIMIC-III datasets. The Perceiver model significantly outperforms the baselines and reduces the computational complexity, as compared with the transformer model, without significant loss of performance. The carefully designed experiments to study irregularity in healthcare also show that the continuous patient state models outperform the baselines. The code is publicly released and verified at https://codeocean.com/capsule/4587224.
使用神经过程进行数据预处理，对个性化生命体征时间序列数据进行建模
影响因子：
7.7
作者：
Pulkit Sharma;Farah E. Shamout;V. Abrol;D. Clifton
通讯作者：
D. Clifton
可解释的多项式神经常微分方程。
DOI：
10.1063/5.0130803
发表时间：
2023
期刊：
Chaos (Woodbury, N.Y.)
影响因子：
--
作者：
Fronk,Colby;Petzold,Linda
通讯作者：
Petzold,Linda
影响因子：
9.8
作者：
Harutyunyan, Hrayr;Khachatrian, Hrant;Galstyan, Aram
通讯作者：
Galstyan, Aram