关于众包和人类在循环工作流程的观点 精确健康的工作流程
1Information and Computer Sciences, University of Hawaii at Manoa, Honolulu, HI, United States.
Journal of medical Internet research
|April 11, 2024
概括
众包增强了复杂健康状况的机器学习诊断,通过使用人类注释来提高模型准确性. 这种方法可以加快查和获得细微条件的护理.
科学领域:
- 医疗保健中的人工智能
- 计算精神病学是一种计算精神病学.
- 人与计算机的交互
背景情况:
- 机器学习模型为健康诊断提供了高性能,但与复杂,高维和非线性数据作斗争.
- 过度装配是一个重大挑战,特别是对于以主观标准预测行为和精神疾病的诊断系统.
研究的目的:
- 探索群众驱动诊断系统的新兴领域.
- 讨论将众包集成到用于健康诊断的人在循环中的机器学习中.
- 确定这个新兴领域的挑战和机遇.
主要方法:
- 利用众包平台来收集来自群众工作者的复杂行为特征的注释.
- 使用这些注释直接用于诊断或作为诊断机器学习模型的输入.
- 将众包数据集成到人类循环中的机器学习工作流程中.
主要成果:
- 众包提供了一个可扩展的解决方案,用于为复杂的诊断任务生成标记数据.
- 群众驱动系统可以潜在地减轻机器学习模型中的过拟合问题.
- 这种方法可以提高诊断预测的准确性和效率.
结论:
- 众包提供了一个有前途的途径,以改进基于机器学习的诊断,以改善细微的健康状况.
- 仔细考虑众包整合可以加速健康查,诊断和患者获得护理.
- 众包与机器学习之间的协同作用对推进计算精神病学和医疗保健具有重大潜力.
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