Short of a liver biopsy, hepatic disease and drug-induced liver injury are diagnosed and classified from clinical findings, especially laboratory results. It was hypothesized that a healthy hepatic dynamic equilibrium might be modelled by an Ornstein–Uhlenbeck (OU) stochastic process, which might lead to more sensitive and specific diagnostic criteria. Using pooled data from healthy volunteers in pharmaceutical clinical trials, this model was applied using maximum likelihood (ML) methods. It was found that the exponent of the autocorrelation function was proportional to the square root of time rather than time itself, as predicted by the OU model. This finding suggests a stronger autocorrelation than expected and may have important implications regarding the use of laboratory testing in clinical diagnosis, in clinical trial design, and in monitoring drug safety. Besides rejecting the OU hypothesis for liver test homeostasis, this paper presents ML estimates for the multivariate Gaussian distribution for healthy adult males. This work forms the basis for a new approach to mathematical modelling to improve both the sensitivity and specificity of clinical measurements over time.

中文翻译：

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使用修正的均值回复Ornstein-Uhlenbeck过程的肝脏动态平衡模型

缺乏肝活检，肝病和药物性肝损伤可以根据临床发现，尤其是实验室结果进行诊断和分类。有人假设，可以通过Ornstein–Uhlenbeck（OU）随机过程来模拟健康的肝脏动态平衡，这可能会导致更敏感和更具体的诊断标准。在药物临床试验中，使用来自健康志愿者的汇总数据，使用最大似然（ML）方法应用了该模型。如OU模型所预测的，发现自相关函数的指数与时间的平方根成正比，而不是与时间本身成正比。这一发现表明自相关性强于预期，并且可能对在临床诊断，临床试验设计中使用实验室测试产生重要影响。以及监控药物安全性。除了否定肝试验动态平衡的OU假设外，本文还提出了健康成年男性多元高斯分布的最大似然估计。这项工作构成了一种新的数学建模方法的基础，该新方法可以随着时间的推移提高临床测量的敏感性和特异性。