Application of Deep Interpolation Network for Clustering of Physiologic Time Series

• URL: http://arxiv.org/abs/2004.13066v1
• Date: Mon, 27 Apr 2020 18:03:24 GMT
• Title: Application of Deep Interpolation Network for Clustering of Physiologic Time Series
• Authors: Yanjun Li (4)(5), Yuanfang Ren (1)(5), Tyler J. Loftus (2,5), Shounak Datta (1) (5), M. Ruppert (1)(5), Ziyuan Guan (1)(5), Dapeng Wu (4), Parisa Rashidi (3)(5), Tezcan Ozrazgat-Baslanti (1)(5)(6), and Azra Bihorac (3)(5)(6) ((1) Department of Medicine, Division of Nephrology, Hypertension, and Renal Transplantation, University of Florida, Gainesville, FL. (2) Department of Surgery, University of Florida, Gainesville, FL. (3) J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL. (4) NSF Center for Big Learning, University of Florida, Gainesville, FL. (5) Precision and Intelligent Systems in Medicine (PrismaP), University of Florida, Gainesville, FL (6) Sepsis and Critical Illness Research Center, University of Florida, Gainesville, FL. )
• Abstract summary: It is common that the time series vital sign information from patients to be both sparse and irregularly collected. We propose a novel deep network to extract latent representations from sparse and irregularly sampled data. In a heterogeneous cohort of hospitalized patients, a deep network extracted representations from vital sign data measured within six hours hospital admission.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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