一种用于增强短时间序列的新方法，应用于镰刀型红细胞病的疼痛事件
A new method for augmenting short time series, with application to pain events in sickle cell disease
AI总结：
本文提出了一种结合相似统计特性的稀疏时间序列数据增强方法，用于提高参数估计和模型选择的可靠性，并应用于镰刀型红细胞病患者疼痛动态的研究。
摘要：
不同领域（包括但不限于生态学、生物学和医疗保健）的研究人员经常面临数据稀疏的挑战。这种稀疏性可能导致不确定性、估计困难和建模中的潜在偏差。本文介绍了一种新颖的数据增强方法，该方法在多个稀疏时间序列数据集共享相似统计特性时结合它们，从而提高参数估计和模型选择的可靠性。我们通过验证研究比较了Hawkes过程和泊松过程，随后将其应用于镰刀型红细胞病（SCD）患者主观疼痛动态的分析，SCD是一种影响全球数百万人的疾病，特别是非洲、地中海、中东和印度裔人群。
英文摘要：
Researchers across different fields, including but not limited to ecology, biology, and healthcare, often face the challenge of sparse data. Such sparsity can lead to uncertainties, estimation difficulties, and potential biases in modeling. Here we introduce a novel data augmentation method that combines multiple sparse time series datasets when they share similar statistical properties, thereby improving parameter estimation and model selection reliability. We demonstrate the effectiveness of this approach through validation studies comparing Hawkes and Poisson processes, followed by application to subjective pain dynamics in patients with sickle cell disease (SCD), a condition affecting millions worldwide, particularly those of African, Mediterranean, Middle Eastern, and Indian descent.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
