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Application of state-space model with skew-t measurement noise to blood test value prediction

机译：状态空间模型对血液检测值预测的挠度测量噪声在血液检测值预测中的应用

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摘要

State-space models (SSMs) have been widely used for analyzing time-series data in the fields of economics and bioinformatics to express the dynamic behavior of data. Recently, filtering and smoothing algorithms applied to linear discrete SSMs with skewed and heavy-tailed measurement noise have been proposed for a more appropriate model because measurement noise does not often follow a Gaussian distribution. In this paper, we propose a linear SSM with skew-t measurement noise for predicting blood test values, along with a method for estimating their parameter values to ensure consistency with the data when using a generalized expectation-maximization (EM) algorithm. To validate the effectiveness of the proposed model and method, we analyze time-series blood test data using both Gaussian and skew-t measurement noise and compared their prediction accuracy for future values. Then, we predicted future blood test values of the unhealthy participant under his current and improved lifestyles. By comparing these predicted results under different lifestyles, we demonstrate that he will overcome lifestyle-related diseases with the improved lifestyle.
机译：状态空间模型（SSMS）已广泛用于分析经济学和生物信息学领域的时间序列数据，以表达数据的动态行为。最近，已经提出了应用于具有偏斜和重尾测量噪声的线性离散SSM的过滤和平滑算法，以获得更合适的模型，因为测量噪声通常不遵循高斯分布。在本文中，我们提出了一种具有Skew-T测量噪声的线性SSM，用于预测血液测试值，以及用于估计其参数值的方法，以确保在使用广义期望最大化（EM）算法时与数据的一致性。为了验证所提出的模型和方法的有效性，我们使用高斯和Skew-T测量噪声分析时间序列血液测试数据，并将其预测精度与未来值进行比较。然后，我们在其目前和改进的生活方式下预测了不健康参与者的未来血液测试价值。通过将这些预测结果与不同的生活方式进行比较，我们证明他将通过改善的生活方式克服生活方式相关的疾病。

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  • 来源
    《Applied Mathematical Modelling》 |2021年第12期|365-378|共14页
  • 作者

    Takanori Hasegawa; Rui Yamaguchi; Masanori Kakuta; Masataka Ando; Jung Songee; Itoyo Tokuda; Koichi Murashita; Seiya Imoto;

  • 作者单位

    Health Intelligence Center The Institute of Medical Science The University of Tokyo 4-6-1 Shirokanedai Minato-ku Tokyo Japan;

    Human Genome Center The Institute of Medical Science The University of Tokyo 4-6-1 Shirokanedai Minato-ku Tokyo Japan;

    Human Genome Center The Institute of Medical Science The University of Tokyo 4-6-1 Shirokanedai Minato-ku Tokyo Japan;

    Department of Diet and Health Sciences Graduate School of Medicine Hirosaki University 5 Zaifu-cho Hirosaki Aomori Japan;

    Department of Health and Beauty Science Graduate School of Medicine Hirosaki University 5 Zaifu-cho Hirosaki Aomori Japan;

    Department of Oral Healthcare Science Graduate School of Medicine Hirosaki University 5 Zaifu-cho Hirosaki Aomori Japan;

    COI Research Initiatives Organization Hirosaki University 5 Zaifu-cho Hirosaki Aomori Japan;

    Health Intelligence Center The Institute of Medical Science The University of Tokyo 4-6-1 Shirokanedai Minato-ku Tokyo Japan;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Electronic health record; Time-series; State-space model; skew-t measurement noise; Generalized expectation-maximization;

    机译：电子健康记录;时间序列;状态空间模型;歪斜测量噪声;广义期望 - 最大化;

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