审查医疗保健中的多式联机机器学习方法
Felix Krones1, Umar Marikkar2, Guy Parsons1
1Oxford Internet Institute, University of Oxford, Oxford, UK.
概括
多模式机器学习集成了各种医疗保健数据,如图像和文本,以增强临床决策. 这种方法更好地反映了医生如何使用多个信息源来准确诊断.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 计算生物学 计算生物学
背景情况:
- 医疗保健中的传统机器学习通常使用单个数据类型,这并不反映全面的临床实践.
- 临床医生将人口统计,实验室,生命体征和成像数据整合在一起,以便做出明智的决策.
研究的目的:
- 提供医疗保健中的多式联机机器学习 (MML) 方法的概述.
- 讨论MML模型开发阶段 (预培训,微调,评估) 和数据融合技术.
- 审查现有的多模式临床数据集和研究,重点关注综合数据模式.
主要方法:
- 文献综述50多项研究,重点关注医疗保健中的多式联机机器学习.
- 分析了17个多模式临床数据集,详细说明了使用的数据模式 (成像,文本,时间序列,表格).
- 探索数据融合策略及其相关挑战和好处.
主要成果:
- 多模式机器学习在改善各种医疗保健应用中的预测准确性方面显示出希望.
- MML方法的有效性在很大程度上取决于特定的数据集和临床任务.
- 图像和表格数据的整合是当前研究的主要重点.
结论:
- 多模式机器学习为医疗分析提供了更全面的方法,模仿临床推理.
- 仔细考虑数据类型和任务要求对于成功实施MML至关重要.
- 对各种数据融合技术的进一步研究可以释放临床决策支持系统的更大潜力.
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