Multimodal Data Integration Enhance Longitudinal Prediction of New-Onset Systemic Arterial Hypertension Patients with Suspected Obstructive Sleep Apnea

Yi Yang, Haibing Jiang, Haitao Yang, Xiangeng Hou, Tingting Wu, YingBing Pan, Xiang Xie
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Abstract

Background : It is crucial to accurately predict the disease progression of systemic arterial hypertension in order to determine the most effective therapeutic strategy. To achieve this, we have employed a multimodal data-integration approach to predict the longitudinal progression of new-onset systemic arterial hypertension patients with suspected obstructive sleep apnea (OSA) at the individual level. Methods : We developed and validated a predictive nomogram model that utilizes multimodal data, consisting of clinical features, laboratory tests
多模态数据整合增强了对疑似阻塞性睡眠呼吸暂停的新发系统性动脉高血压患者的纵向预测能力
背景:为了确定最有效的治疗策略,准确预测系统性动脉高血压的病情发展至关重要。为此,我们采用了一种多模态数据整合方法,从个体层面预测疑似阻塞性睡眠呼吸暂停(OSA)的新发系统性动脉高血压患者的纵向进展。方法:我们开发并验证了一个预测性提名图模型,该模型利用多模态数据,包括临床特征、实验室测试
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