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机器学习在淋巴瘤管理中的应用:现行实践和未来前景

Junyun Yuan¹, Ya Zhang^1,2,3, Xin Wang^1,2,3,4,5

¹Department of Hematology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.

|April 17, 2024

概括

机器学习 (ML) 通过分析复杂的医疗数据来增强淋巴瘤诊断. 这项技术提供了更准确的病理分析,个性化治疗和改善患者预后.

科学领域:

在瘤学瘤学.
医疗信息学医疗信息学
计算生物学计算生物学

背景情况:

医疗记录的数字化和多态数据在淋巴瘤研究中创造了高维数据集.
整合多组学和医学成像数据有助于临床决策.
机器学习 (ML) 是分析大规模数据集和识别复杂模式的关键工具.

研究的目的:

审查最近的ML技术在淋巴瘤诊断,治疗和预后的进展.
突出ML在分析淋巴瘤数据中的工作流程.
澄清临床淋巴瘤实践中ML的局限性和未来方向.

主要方法:

在淋巴瘤中ML应用的综合文献综述.
分析ML技术用于病理图像分析和多组数据集成.
讨论ML在诊断,治疗选择和预后预测中的作用.

主要成果:

ML促进了从定性到定量病理分析的转变,提高了准确性和客观性.
人工智能和数字采矿在分析淋巴瘤病理图像方面表现有前途.
ML有助于对淋巴瘤患者进行准确的诊断,预后信息和个性化治疗策略.

结论:

ML技术正在推动淋巴瘤的诊断,治疗和预后.
该审查澄清了ML在临床淋巴瘤管理中的当前能力和未来潜力.
需要进一步的研究才能将ML完全纳入常规临床实践,以获得最佳的患者结果.

关键词:

人工智能的人工智能是人工智能.诊断诊断诊断诊断诊断诊断淋巴瘤淋巴瘤是什么机器学习是机器学习.预后预后预后治疗治疗治疗治疗治疗治疗

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