概率断棒模型：不规则样本数据的回归算法及其在eGFR中的应用
Probabilistic Broken-Stick Model: A Regression Algorithm for Irregularly Sampled Data with Application to eGFR
摘要：
为了使临床医生能够管理疾病进展并就药物剂量、治疗方案或安排随访预约做出有效决策，有必要能够识别重复生物医学测量的短期和长期趋势。然而，这是复杂的，因为这些测量是不规则的采样，并受到真正的生理变化和外部因素的影响。在其当前形式中，现有的回归算法通常不能满足临床医生识别短期事件同时仍然能够识别疾病进展的长期趋势的所有要求。因此，为了平衡短期的可解释性和长期的灵活性，提出了一种扩展的破棒回归模型，以使它们更适合于临床时间序列的建模。建议的概率断棒模型可以鲁棒地估计短期和长期的趋势，同时也适应临床时间序列的不等长和不规则采样的性质。此外，由于该模型是参数化的，完全是生成性的，其一阶导数比线性回归更可靠地提供了对计量年度变化率的长期非线性估计。所提出的模型的好处说明使用估计肾小球滤过率作为一个案例研究管理慢性肾脏疾病患者。
英文摘要：
In order for clinicians to manage disease progression and make effective decisions about drug dosage, treatment regimens or scheduling follow up appointments, it is necessary to be able to identify both short and long-term trends in repeated biomedical measurements. However, this is complicated by the fact that these measurements are irregularly sampled and influenced by both genuine physiological changes and external factors. In their current forms, existing regression algorithms often do not fulfil all of a clinician's requirements for identifying short-term events while still being able to identify long-term trends in disease progression. Therefore, in order to balance both short term interpretability and long term flexibility, an extension to broken-stick regression models is proposed in order to make them more suitable for modelling clinical time series. The proposed probabilistic broken-stick model can robustly estimate both short-term and long-term trends simultaneously, while also accommodating the unequal length and irregularly sampled nature of clinical time series. Moreover, since the model is parametric and completely generative, its first derivative provides a long-term non-linear estimate of the annual rate of change in the measurements more reliably than linear regression. The benefits of the proposed model are illustrated using estimated glomerular filtration rate as a case study for managing patients with chronic kidney disease.
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