基于深度学习的心血管病急诊患者运动处方开发与分析
Journal: Advances in Computer and Autonomous Intelligence Research DOI: 10.12238/acair.v2i4.10292
Abstract
目的：利用深度学习技术为急诊心血管疾病(CVD)患者开发个性化运动处方。方法：通过三维度健康评估方法生成个性化运动处方。利用CNN和RNN组合生成深度学习模型,选取200名急诊CVD患者,随机分为实验组和对照组。实验为期12周,每4周评估一次。结果：在深度学习模型训练后,运动处方信息归类准确度达88.5%,其中实验组在心率、收缩压、6分钟步行测试(6MWT)和最大摄氧量(VO₂Max)等关键指标上显示出轻微的优势,特别是在收缩压和心率方面表现更为显著。结论：基于深度学习生成的个性化运动处方能够有效提升急诊CVD患者的心血管功能、运动耐力及依从性。
Keywords
心血管疾病；深度学习；运动处方；急诊
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