用处方数据推断慢性病治疗起点，更准且避免过早误判。
Inferring Chronic Treatment Onset from ePrescription Data: A Renewal Process Approach
- 将处方行为建模为更新过程，通过变化点检测区分间歇与持续用药
- 在240万患者数据中显著减少左删失导致的早期误判，提升时间合理性
- 适合研究疾病起始时间但需关注处方密度对效果的影响
纵向电子健康记录（EHR）数据常存在左删失问题，导致诊断记录不完整且不可靠，难以准确判断疾病起始时间。相比之下，门诊处方形成具有更新特性的连续轨迹，能反映疾病管理的动态过程。本文提出一种概率框架，将处方动态建模为更新过程，通过检测基线泊松（间歇性开药）与特定韦布尔（持续治疗）更新模型之间的变化点，识别从间歇到持续治疗的转变。基于包含240万个体的全国性电子处方数据集，实验表明该方法生成的起始时间估计比简单规则法更具时间合理性，在强左删失条件下显著减少不合理的早期检测。不同疾病的检测性能差异明显，且与处方密度高度相关，揭示了基于治疗推断起始时间的优势与局限。
原文摘要 · Abstract (English)
Longitudinal electronic health record (EHR) data are often left-censored, making diagnosis records incomplete and unreliable for determining disease onset. In contrast, outpatient prescriptions form renewal-based trajectories that provide a continuous signal of disease management. We propose a probabilistic framework to infer chronic treatment onset by modeling prescription dynamics as a renewal process and detecting transitions from sporadic to sustained therapy via change-point detection between a baseline Poisson (sporadic prescribing) regime and a regime-specific Weibull (sustained therapy) renewal model. Using a nationwide ePrescription dataset of 2.4 million individuals, we show that the approach yields more temporally plausible onset estimates than naive rule-based triggering, substantially reducing implausible early detections under strong left censoring. Detection performance varies across diseases and is strongly associated with prescription density, highlighting both the strengths and limits of treatment-based onset inference.
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