机器学习方法在儿科瘤学中的作用:系统性审查
Nojoud Noureldayim Elsayid1, Elwaleed Idrees Aydaross Adam2, Samah Mohamed Yousif Mahmoud1
1Pediatrics, Children Hospital, Ministry of Health, Riyadh, SAU.
Cureus
|January 17, 2025
概括
机器学习 (ML) 在儿科瘤学中显示出有前途的改善诊断和治疗. 然而,对于更广泛的临床使用,需要解决数据和验证有限等挑战.
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:
- 了解医疗和生物风险变量对于改善儿科癌症患者的治疗结果至关重要.
- 越来越多的儿科癌症数据需要先进的分析方法,如机器学习 (ML).
研究的目的:
- 系统地审查儿童瘤学中机器学习应用的当前状态.
- 确定 ML 在现场的发展,前景和挑战.
主要方法:
- 在四个数据库 (Scopus,科学网,PubMed,Cochrane图书馆) 中按照PRISMA指南进行系统的文献搜索.
- 在初步检索1536项研究和随后的资格评估后,包括42项研究.
- 研究按瘤类型 (液体,固体,中枢神经系统) 和ML应用目标 (分类,预测,优化) 分类.
主要成果:
- 儿科瘤学中的ML应用侧重于分类,治疗反应预测和剂量优化.
- 使用的技术包括神经网络,k-最近邻居,随机森林,支向量机器和 naive Bayes.
- 在治疗反应预测和自动化分析中确定了优势,通常与医生的表现相匹配或超过.
结论:
- 机器学习具有显著的潜力,可以改善儿科癌症诊断,决策和监测.
- 目前面临的挑战包括临床适用性,报告标准,小样本大小和缺乏外部验证的差异.
- 需要使用标准化的方法和建议进行进一步的研究,以最大限度地提高ML在儿科瘤学中的治疗适用性.
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