Human-in-the-loop machine learning with applications for population health
Human-in-the-loop machine learning with applications for population health
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DOI：
10.1007/s42486-022-00115-4
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发表时间：
2022-12
影响因子：
2.1
通讯作者：
Long Chen;Jiangtao Wang;Bin Guo;Liming Chen
中科院分区：
文献类型：
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作者：
Long Chen;Jiangtao Wang;Bin Guo;Liming Chen
虽然人工智能和机器学习的技术进步使许多有前途的智能系统成为可能，但许多计算任务仍然不能完全由机器智能来完成。在人类和机器智能互补性的推动下，一个新兴的趋势是让人类参与机器学习和决策的循环。在这篇文章中，我们提供了宏观和微观的人在环机器学习的回顾。我们首先描述主要的机器学习挑战，这些挑战可以通过人类在循环中的干预来解决。然后，我们仔细检查了将人类引入机器学习生命周期的每个步骤的最新研究和发现。接下来，介绍了我们最近在人在环机器学习人口健康方面的应用研究的一个案例。最后，分析了目前的研究差距，并指出了未来的研究方向。
Though technical advance of artificial intelligence and machine learning has enabled many promising intelligent systems, many computing tasks are still not able to be fully accomplished by machine intelligence. Motivated by the complementary nature of human and machine intelligence, an emerging trend is to involve humans in the loop of machine learning and decision-making. In this paper, we provide a macro–micro review of human-in-the-loop machine learning. We first describe major machine learning challenges which can be addressed by human intervention in the loop. Then we examine closely the latest research and findings of introducing humans into each step of the lifecycle of machine learning. Next, a case study of our recent application study in human-in-the-loop machine learning for population health is introduced. Finally, we analyze current research gaps and point out future research directions.