机器学习在骨髓质疏松症候群中的潜在承诺
Valeria Visconte1, Jaroslaw P Maciejewski1, Luca Guarnera2
1Department of Translational Hematology & Oncology Research, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA.
Seminars in hematology
|November 30, 2024
概括
人工智能 (AI) 和机器学习 (ML) 在诊断和预测骨髓瘤 (MN) 的结果方面表现有前途. 然而,临床医生熟悉和患者关注等挑战阻碍了这些强大的工具的临床采用.
科学领域:
- 生物医学研究的研究.
- 血液性恶性瘤 血液性恶性瘤
- 骨髓瘤瘤 (MN) 是一种神经瘤.
背景情况:
- 人工智能 (AI) 和机器学习 (ML) 正在改变临床研究,包括血液恶性瘤和MN.
- 机器学习的应用包括诊断,结果预测,决策树和集群.
研究的目的:
- 审查ML机制和应用在mn和骨髓质综合征.
- 突出ML在临床实践中的优点和局限性.
- 在临床环境中解决ML管道实施的潜力.
主要方法:
- 在MN中对ML应用的文献综述.
- 分析ML在风险分层和结果预测方面的潜力.
- 讨论临床翻译的障碍.
主要成果:
- 机器学习证明了在机器学习中增强诊断和结果预测的潜力.
- 尽管有希望的结果,但目前没有ML管道在广泛的临床实践中.
- 临床医生的不熟悉和患者的担忧 (隐私,可靠性,人与人互动) 构成重大挑战.
结论:
- 机器学习为完善风险分层和提高机器学习分类和结果预测的准确性提供了巨大的潜力.
- 解决临床医生的怀疑和患者的担忧对于成功实施ML至关重要.
- 需要进一步的研究和开发,以弥合在MN的ML潜力和临床现实之间的差距.
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