用数据模型分析长新冠风险因素，预测症状严重程度
Statistical and Predictive Analysis to Identify Risk Factors and Effects of Post COVID-19 Syndrome
- 采用神经网络等方法分析多类因素对长新冠的影响
- 神经网络预测误差仅19%，表现最优
- 嗅觉丧失、头痛等是关键预测指标，适合临床干预研究
根据最新研究，部分新冠肺炎症状可能在感染后持续数月，形成所谓长新冠。疫苗接种时间、患者特征及急性期症状等因素可能影响长新冠的持续时间和严重程度。每位患者因个体因素组合不同，表现出特定的风险或症状强度。本文旨在实现两个目标：（1）通过统计分析识别各类因素与长新冠之间的关系；（2）基于这些因素进行长新冠严重程度的预测。我们利用Lifelines新冠队列数据，对比并解释了线性模型、随机森林、梯度提升和神经网络等多种数据驱动方法。结果表明，神经网络在平均绝对百分比误差（MAPE）上表现最佳，预测平均误差为19%。可解释性分析揭示嗅觉丧失、头痛、肌肉疼痛以及疫苗接种时间是重要预测因子，而慢性病史和性别则是关键风险因素。这些发现为理解长新冠机制和制定针对性干预策略提供了重要依据。
原文摘要 · Abstract (English)
Based on recent studies, some COVID-19 symptoms can persist for months after infection, leading to what is termed long COVID. Factors such as vaccination timing, patient characteristics, and symptoms during the acute phase of infection may contribute to the prolonged effects and intensity of long COVID. Each patient, based on their unique combination of factors, develops a specific risk or intensity of long COVID. In this work, we aim to achieve two objectives: (1) conduct a statistical analysis to identify relationships between various factors and long COVID, and (2) perform predictive analysis of long COVID intensity using these factors. We benchmark and interpret various data-driven approaches, including linear models, random forests, gradient boosting, and neural networks, using data from the Lifelines COVID-19 cohort. Our results show that Neural Networks (NN) achieve the best performance in terms of MAPE, with predictions averaging 19\% error. Additionally, interpretability analysis reveals key factors such as loss of smell, headache, muscle pain, and vaccination timing as significant predictors, while chronic disease and gender are critical risk factors. These insights provide valuable guidance for understanding long COVID and developing targeted interventions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。