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在状细胞病患者数据中探索机器学习算法:系统性审查

Tiago Fernandes Machado1, Francisco das Chagas Barros Neto2, Marilda de Souza Gonçalves2,3

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科学领域:

  • 生物医学信息学 生物医学信息学
  • 计算生物学 计算生物学
  • 医疗信息学 医疗信息学

背景情况:

  • 状细胞疾病 (SCD) 提出了复杂的诊断和监测挑战.
  • 现有的SCD管理方法需要加强,以改善患者护理和结果.

研究的目的:

  • 系统地审查机器学习 (ML) 算法在状细胞疾病 (SCD) 的应用.
  • 评估ML在SCD诊断,早期器官衰竭检测,药物剂量识别和疼痛强度分类中的作用.

主要方法:

  • 综合的文献搜索和对应用ML到SCD的最新研究的分析.
  • 包括各种ML算法,如多层感知器,支持向量机,随机森林,物流回归,LSTM,ELM,CNN和转移学习.

主要成果:

  • ML技术在诊断和监测SCD方面显示出有希望的结果.
  • 确定的ML应用包括器官衰竭的早期检测和疼痛强度的分类.
  • 各种ML算法显示了推进SCD管理的潜力.

结论:

  • 机器学习在SCD诊断,监测和预后方面具有变革性的潜力.
  • 挑战包括有限的数据集大小,可解释性问题和过度匹配风险.
  • 未来的研究应该专注于更大的数据集,增强的解释性和先进的ML技术,如深度学习.