基于知识的机器学习用于癌症诊断和预后:一篇综述
Lingchao Mao1, Hairong Wang1, Leland S Hu2
1H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA.
概括
基于知识的机器学习 (KIML) 通过将生物医学知识与数据驱动模型集成来提高癌症诊断和预后. 这种方法解决了诸如有限数据的挑战,并提高了模型的准确性和可解释性.
科学领域:
- 癌症学
- 人工智能
- 生物信息学
背景情况:
- 机器学习 (ML) 通过复杂的数据分析来帮助癌症诊断和预后.
- ML模型面临的局限性包括小样本大小,高维数据,患者异质性和可解释性问题.
- 将生物医学知识整合到机器学习模型中可以提高准确性,稳定性和可解释性.
研究的目的:
- 审查用于癌症研究的生物医学知识和数据的最新机器学习研究.
- 研究基于知识的机器学习 (KIML) 在癌症诊断和预后方面的应用.
- 讨论KIML在推进癌症研究和医疗自动化方面的未来方向.
主要方法:
- 对瘤学中的基于知识的机器学习的最新文献进行审查.
- 分析各种知识表现形式和整合策略.
- 在癌症诊断和预后中检查KIML的具体例子.
主要成果:
- 在癌症研究中,KIML具有克服ML局限性的潜力.
- 生物医学知识的成功整合提高了ML模型的性能.
- 代表和整合知识到机器学习管道中存在各种策略.
结论:
- KIML是一种有前途的癌症诊断方法.
- 对KIML的进一步研究可以提高瘤学中的医疗自动化.
- 一个不断发展的在线资源可用于支持KIML癌症研究.
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