Zheng Wenyao, Chen Yun-Hsuan, Sawan Mohamad
CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou 310024, China.
Institute of Advanced Study, Westlake Institute for Advanced Study, Hangzhou 310024, China.
Healthcare (Basel). 2022 Oct 27;10(11):2134. doi: 10.3390/healthcare10112134.
Stroke risk prediction based on electronic health records is currently an important research topic. Previous research activities have generally used single-time physiological data to build static models and have focused on algorithms to improve prediction accuracy. Few studies have considered historical measurements from a data perspective to construct dynamic models. Since it is a chronic disease, the risk of having a stroke increases and the corresponding risk factors become abnormal when healthy people are diagnosed with a stroke. Therefore, in this paper, we applied longitudinal data, with the backward joint model, to the Chinese Longitudinal Healthy Longevity and Happy Family Study's dataset to monitor changes in individuals' health status precisely on time and to increase the prediction accuracy of the model. The three-year prediction accuracy of our model, considering three measurements of longitudinal parameters, is 0.926. This is higher than the traditional Cox proportional hazard model, which has a 0.833 prediction accuracy. The results obtained in this study verified that longitudinal data improves stroke risk prediction accuracy and is promising for dynamic stroke risk prediction and prevention. Our model also verified that the frequency of fruit consumption, erythrocyte hematocrit, and glucose are potential stroke-related factors.
基于电子健康记录的中风风险预测是当前一个重要的研究课题。以往的研究活动通常使用单次生理数据来构建静态模型，并专注于提高预测准确性的算法。很少有研究从数据角度考虑历史测量值来构建动态模型。由于中风是一种慢性病，当健康人被诊断出中风时，患中风的风险会增加，相应的风险因素也会变得异常。因此，在本文中，我们将纵向数据与反向联合模型应用于中国老年健康长寿和幸福家庭纵向研究数据集，以准确及时地监测个体健康状况的变化，并提高模型的预测准确性。考虑纵向参数的三次测量，我们模型的三年预测准确率为0.926。这高于传统的Cox比例风险模型，其预测准确率为0.833。本研究获得的结果证实，纵向数据提高了中风风险预测的准确性，在动态中风风险预测和预防方面具有前景。我们的模型还证实，水果摄入量、红细胞压积和血糖频率是潜在的中风相关因素。
