Application of Deep Interpolation Network for Clustering of Physiologic Time Series
Application of Deep Interpolation Network for Clustering of Physiologic Time Series
复制标题
深度插值网络在生理时间序列聚类中的应用
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
A. Bihorac
中科院分区：
文献类型：
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作者：
Yanjun Li;Yuanfang Ren;T. Loftus;Shounak Datta;M. Ruppert;Ziyuan Guan;D. Wu;Parisa Rashidi;T. Ozrazgat;A. Bihorac
背景资料：在入院的早期阶段，临床医生必须使用有限的信息来做出诊断和治疗决策，因为患者的病情发展。然而，常见的是，来自患者的时间序列生命体征信息既稀疏又不规则地收集，这对机器/深度学习技术分析和促进临床医生改善人类健康结果提出了重大挑战。为了解决这个问题，我们提出了一种新的深度插值网络，从入院后6小时内测量的稀疏和不规则采样的时间序列生命体征中提取潜在表示。研究方法：我们为所有（n= 75，762）入住三级医疗中心持续6小时或更长时间的成年患者创建了一个电子健康记录数据的单中心纵向数据集，使用55%的数据集进行训练，23%用于验证，22%用于测试。在入院后6小时内提取6个生命体征（收缩压、舒张压、心率、体温、血氧饱和度和呼吸频率）的所有原始时间序列。提出了一种深度插值网络来从这种不规则的稀疏多变量时间序列数据中学习，以提取固定的低维潜在模式。我们使用k-means聚类算法聚类的病人入院导致7个集群。调查结果：培训、验证和测试队列的年龄（55-57岁）、性别（55%女性）和入院生命体征相似。确定了七个不同的集群。M解释：在住院患者的异质队列中，深度插值网络从入院后6小时内测量的生命体征数据中提取表示。这种方法可能对时间限制和不确定性下的临床决策支持具有重要意义。
Background: During the early stages of hospital admission, clinicians must use limited information to make diagnostic and treatment decisions as patient acuity evolves. However, it is common that the time series vital sign information from patients to be both sparse and irregularly collected, which poses a significant challenge for machine / deep learning techniques to analyze and facilitate the clinicians to improve the human health outcome. To deal with this problem, We propose a novel deep interpolation network to extract latent representations from sparse and irregularly sampled time-series vital signs measured within six hours of hospital admission. Methods: We created a single-center longitudinal dataset of electronic health record data for all (n=75,762) adult patient admissions to a tertiary care center lasting six hours or longer, using 55% of the dataset for training, 23% for validation, and 22% for testing. All raw time series within six hours of hospital admission were extracted for six vital signs (systolic blood pressure, diastolic blood pressure, heart rate, temperature, blood oxygen saturation, and respiratory rate). A deep interpolation network is proposed to learn from such irregular and sparse multivariate time series data to extract the fixed low-dimensional latent patterns. We use k-means clustering algorithm to clusters the patient admissions resulting into 7 clusters. Findings: Training, validation, and testing cohorts had similar age (55-57 years), sex (55% female), and admission vital signs. Seven distinct clusters were identified. M Interpretation: In a heterogeneous cohort of hospitalized patients, a deep interpolation network extracted representations from vital sign data measured within six hours of hospital admission. This approach may have important implications for clinical decision-support under time constraints and uncertainty.
影响因子：
120.7
作者：
Seymour, Christopher W.;Kennedy, Jason N.;Angus, Derek C.
通讯作者：
Angus, Derek C.