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在癌症相关血栓形成中的机器学习:作或希望解开凝块

Rushad Patell^1,2, Jeffrey I Zwicker^3,4, Rohan Singh⁵

¹Division of Medical Oncology and Hematology, Beth Israel Deaconess Medical Center, Boston, MA.

Bleeding, thrombosis and vascular biology

|September 26, 2024

概括

机器学习 (ML) 在医疗保健中提供了变革性的潜力,特别是在癌症相关血栓形成 (CAT) 方面,通过改善诊断和患者的治疗结果. 解决数据质量和偏差对于在医学中有效实施ML至关重要.

科学领域:

医疗信息学医疗信息学
计算生物学计算生物学
人工智能在医学中的应用

背景情况:

机器学习 (ML) 利用大型数据集创建算法,通过提高患者护理和临床医生的效率来彻底改变医疗保健.
在医学中的ML应用可以提供机械洞察力,改善患者的治疗结果,简化医疗保健服务,并推动创新.
癌症相关血栓形成 (CAT) 是一个重要的临床挑战,ML可以带来巨大的好处.

研究的目的:

审查ML在癌症相关血栓瘤 (CAT) 的当前和潜在应用.
探索ML在诊断,病例检测和CAT风险分层中的作用.
确定挑战,并提出在医疗保健中实施ML的合作策略.

主要方法:

对医疗保健中的ML应用现有文献的审查,重点是CAT.
将ML应用分类为放射学计算机视觉,电子健康记录的自然语言处理和CAT的预测算法.
对医学领域的ML的挑战和机会的分析.

主要成果:

机器学习的应用包括用于血栓塞瘤诊断的计算机视觉,用于电子健康记录中的病例检测的NLP以及用于CAT风险分层的预测算法.
关键的挑战包括数据质量,过度匹配,概括性,偏差传播和缺乏透明度.
临床医生,计算机科学家,监管机构和患者团体之间的合作是必不可少的.

结论:

ML对促进癌症相关血栓的理解和管理具有重大前景.
克服与数据和实施相关的挑战对于实现ML在医疗保健中的全部潜力至关重要.
多学科合作对于确保在临床实践中负责任有效地部署ML工具至关重要.

关键词:

与癌症相关的血栓形成.机器学习是机器学习.自然语言处理自然语言处理.静脉血栓塞栓症是一种静脉血栓塞栓.

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