机器学习在急性冠状动脉综合征中的应用:诊断,结果和管理
Shanshan Nie1, Shan Zhang2, Yuhang Zhao3
1Department of Cardiovascular Disease, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, 450000, Henan, China.
Advances in therapy
|December 6, 2024
概括
机器学习 (ML) 通过改善急性冠状动脉综合征 (ACS) 诊断和预后,推进心血管保健. 将ML与智能设备集成为更好的急性心肌梗塞 (AMI) 管理提供实时患者监测.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 急性冠状动脉综合征 (ACS) 仍然是全球主要的死亡原因.
- 及时准确诊断急性心肌梗塞 (AMI) 对患者的治疗结果至关重要.
- 机器学习 (ML) 研究越来越多地为了解和管理ACS.
研究的目的:
- 审查ML在ACS诊断,预后,风险因素识别和管理中的应用.
- 探索ML与智能设备的集成,以进行动态患者评估.
- 讨论ACS中ML的当前挑战和未来方向.
主要方法:
- 对ACS.应用的当前ML算法的审查.
- 数据模式的分析,包括ECG,EMR,成像和奥米克.
- 检查与可穿戴设备,智能手机和传感器的ML集成.
主要成果:
- ML用于ACS检测,预后预测,治疗管理和生物标志物发现.
- 智能设备增强了ACS患者的实时监测和临床护理.
- 在ACS管理的不同方面使用各种ML算法.
结论:
- ML具有显著的潜力,可以提高ACS的诊断,预后和管理.
- 未来的ML开发应该解决当前面临的挑战,以实现更广泛的临床采用.
- 机器学习和智能设备之间的协同作用有望改善心脏病学中的动态患者评估.
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