Application of state-space model with skew-t measurement noise to blood test value prediction
Application of state-space model with skew-t measurement noise to blood test value prediction
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DOI：
10.1016/j.apm.2021.08.007
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发表时间：
2021-08-29
影响因子：
5
通讯作者：
Imoto，Seiya
中科院分区：
文献类型：
--
作者：
Hasegawa，Takanori;Yamaguchi，Rui;Imoto，Seiya
状态空间模型（SSM）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，由于测量噪声通常不遵循高斯分布，因此提出了应用于具有偏斜和重尾测量噪声的线性离散 SSM 的滤波和平滑算法，以获得更合适的模型。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，以及一种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，我们使用高斯和偏斜t测量噪声来分析时间序列血液测试数据，并比较它们对未来值的预测准确性。然后，我们预测了不健康的参与者在当前和改善的生活方式下的未来血液检查值。通过比较不同生活方式下的这些预测结果，我们证明他将通过改善生活方式来克服与生活方式相关的疾病。
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.