研究中心一篇论文“A conformal regressor for predicting negative conversion time of Omicron patients”被Medical & Biological Engineering & Computing刊发。

【摘要】根据Omicron的情况和特点，国家不断优化COVID-19的防控规则。全球疫情仍在蔓延，中国境内不断出现新的感染病例。为了方便感染者估计病毒感染的过程，本文提出了一种预测负转化时间的预测模型。回顾性研究2022年上半年山东省Omicron感染患者的临床特征。这些特征按疾病诊断结果、临床体征、中医症状和用药情况进行分组。这些特征被输入到 eXtreme Gradient Boosting (XGBoost) 模型，输出是预测的负转化天数。同时，采用XGBoost作为共形预测（CP）框架的底层算法，可以实现误差率可控的概率区间估计。结果表明，该模型的平均绝对误差为3.54天，具有最短间隔的预测结果。这说明本文的方法能够携带更多的决策信息，在一定程度上帮助人们更好地了解疾病、自我评估病程。

【Abstract】 In light of the situation and the characteristics of Omicron, the country has continuously optimized the rules for the prevention and control of COVID-19. The global epidemic is still spreading, and new cases of infection continue to emerge in China. To facilitate the infected person to estimate the course of virus infection, a prediction model for predicting negative conversion time is proposed in this article. The clinical features of Omicron-infected patients in Shandong Province in the first half of 2022 are retrospectively studied. These features are grouped by disease diagnosis result, clinical sign, traditional Chinese medicine symptoms, and drug use. These features are input to the eXtreme Gradient Boosting (XGBoost) model, and the output is the predicted number of negative conversion days. At the same time, XGBoost is used as the underlying algorithm of the conformal prediction (CP) framework, which can realize the probability interval estimation with a controllable error rate. The results show that the proposed model has a mean absolute error of 3.54 days and has the shortest interval prediction result. This shows that the method in this paper can carry more decision-making information and help people better understand the disease and self-estimate the course of the disease to a certain extent.