规范性分析决策系统用于长期COVID患者的心血管疾病预测,使用先进的强化学习算法
1Department of Computer Science and Engineering, C.S.I. Institute of Technology, Thovalai, Tamilnadu, India.
一个新的AI系统预测了长期COVID患者的心血管疾病 (CVD) 准确率为97.88%. 这种决策工具有助于早期检测和个性化干预,用于COVID后恢复.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 心脏病学 心脏病学
背景情况:
- COVID-19可能会引起广泛的炎症,增加患有慢性病如心血管疾病 (CVD) 的人的风险.
- 早期发现心血管疾病对于及时干预和预防严重并发症至关重要,但仍然具有挑战性,特别是在长期COVID患者中.
- 有限的数据存在于COVID-19对患有长期症状的患者心血管疾病的特定影响.
研究的目的:
- 为长期COVID患者量身定制的CVD预测引入一种新的决策系统.
- 为了利用一个改进的双重注意力残留双向封闭循环神经网络单元 (DA-ResBiGRU) 算法与AI-Biruni地球半径优化 (ABER) 进行增强的风险评估.
- 通过准确评估个体患者的风险概况,为医疗保健提供者提供一个及时和有针对性的干预工具.
主要方法:
- 使用DA-ResBiGRU算法和ABER进行心血管疾病预测的新型决策系统的开发.
- 实时监测和分析患者数据中的复杂模式,以评估个体风险概况.
- 从新患者数据中不断学习,以确保最新和适应性预测.
主要成果:
- 与现有方法 (DNN,LSTM,Inception-v3,Xception,MobileNetV2) 相比,拟的DA-ResBiGRU与ABER算法取得了更高的性能.
- 该系统显示了高精度 (97.88%),灵敏度 (95.50%),特异性 (94.29%),精度 (96.68%) 和F测量 (95.85%).
- 模拟结果表明,该算法有可能融入临床决策,以有效识别高风险患者.
结论:
- 开发的AI系统为长期COVID患者的个性化心血管疾病风险评估提供了一个有希望的方法.
- 该系统可以显著帮助医疗保健专业人员识别有风险的个体,使心血管并发症的积极管理.
- 这项研究解决了监测和管理与长期COVID相关的心血管风险的关键差距.
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