首页> 外文期刊>ACM Transactions on Interactive Intelligent Systems >Visualizing Ubiquitously Sensed Measures of Motor Ability in Multiple Sclerosis: Reflections on Communicating Machine Learning in Practice
【24h】

Visualizing Ubiquitously Sensed Measures of Motor Ability in Multiple Sclerosis: Reflections on Communicating Machine Learning in Practice

机译:可视化多发性硬化症中无所不在的运动能力测量指标:对实践中交流机器学习的思考

获取原文
获取原文并翻译 | 示例

摘要

Sophisticated ubiquitous sensing systems are being used to measure motor ability in clinical settings. Intended to augment clinical decision-making, the interpretability of the machine-learning measurements underneath becomes critical to their use. We explore how visualization can support the interpretability of machine-learning measures through the case of Assess MS, a system to support the clinical assessment of Multiple Sclerosis. A substantial design challenge is to make visible the algorithm's decision-making process in a way that allows clinicians to integrate the algorithm's result into their own decision process. To this end, we present a series of design iterations that probe the challenges in supporting interpretability in a real-world system. The key contribution of this article is to illustrate that simply making visible the algorithmic decision-making process is not helpful in supporting clinicians in their own decision-making process. It disregards that people and algorithms make decisions in different ways. Instead, we propose that visualisation can provide context to algorithmic decision-making, rendering observable a range of internal workings of the algorithm from data quality issues to the web of relationships generated in the machine-learning process.
机译:复杂的无处不在的传感系统已用于测量临床环境中的运动能力。为了增强临床决策能力,下面的机器学习度量的可解释性对于它们的使用至关重要。我们探讨了可视化如何通过Assess MS(一种支持多发性硬化症临床评估的系统)来支持机器学习方法的可解释性。一个重大的设计挑战是以使临床医生将算法的结果集成到他们自己的决策过程中的方式使算法的决策过程可见。为此,我们提出了一系列设计迭代,以探讨在现实系统中支持可解释性方面的挑战。本文的主要贡献是说明,仅使算法决策过程可见是无助于支持临床医生自己的决策过程。它忽略了人和算法以不同的方式做出决策。相反,我们建议可视化可以为算法决策提供上下文,从而使算法的内部工作范围可观察到,从数据质量问题到机器学习过程中生成的关系网。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号