机器学习在心脏瘤学:新兴学科的新见解
Yi Zheng1, Ziliang Chen1, Shan Huang1
1Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, 300211 Tianjin, China.
机器学习 (ML) 通过预测和诊断与癌症治疗有关的心脏问题来帮助心脏瘤学. 本综述探讨了用于风险分层和理解心脏结果差异的ML应用.
科学领域:
- 心脏瘤学 - 心脏瘤学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 瘤疗法可能会导致一系列不良心脏事件,需要专门的护理.
- 心脏瘤学是一个新兴的跨学科领域,专注于治疗癌症患者的心脏并发症.
- 有效的风险分层对于接受癌症治疗的患者至关重要.
研究的目的:
- 审查机器学习 (ML) 方法在心脏瘤学中的应用.
- 通过ML方法确定心脏毒性的总结.
- 讨论ML在解决心脏瘤学差异和未来方向方面的作用.
主要方法:
- 对心脏瘤学中ML应用的综合文献综述.
- 分析利用深度学习,人工神经网络和随机森林算法的研究.
- 对预测,诊断和治疗心脏毒性的ML的研究结果的综合.
主要成果:
- ML越来越多地用于心脏瘤学中的风险分层.
- ML模型已用于预测,诊断和治疗心脏毒性.
- ML可以帮助识别心脏结果中的性别和种族差异.
结论:
- 机器学习在推进心脏瘤学方面提供了巨大的潜力.
- 进一步整合ML对于个性化风险评估和心脏毒性管理至关重要.
- 建立多学科团队和教育ML专业人员对于未来的进步至关重要.
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