Longitudinal Data to Enhance Dynamic Stroke Risk Prediction.

ABSTRACT: 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 predict

SUBMITTER: Zheng W 

PROVIDER: S-EPMC9691140 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature