arXiv 2002.122612020-02cs.HCcs.AIcs.CV

基于机器学习的决策支持系统在脑卒中康复评估中的应用

Opportunities of a Machine Learning-based Decision Support System for Stroke Rehabilitation Assessment

Carnegie Mellon University（卡内基梅隆大学）；Instituto Superior Técnico（高等技术学院）；Madeira Interactive Technology Institute（马德拉互动技术研究所）

Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermúdez i Badia

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摘要：

康复评估对于确定患者的适当干预至关重要。然而，目前的评估实践主要依赖于治疗师的经验，并且由于治疗师的有限可用性，评估很少被执行。在本文中，我们确定了治疗师的需求，以评估患者的功能能力（例如，替代的角度评估与患者的运动动作的定量信息）。因此，我们开发了一个智能决策支持系统，可以使用强化学习来识别评估的显著特征，以评估运动的质量并总结患者的具体分析。我们评估了这个系统与七个治疗师使用的数据集，从15名患者进行三个练习。评估表明，我们的系统是首选的传统系统没有分析，同时提供更多有用的信息，并显着增加了协议治疗师的评价从0.6600到0.7108 F1-分数（$p <0.05$）。我们讨论的重要性，提出上下文相关的和突出的信息和适应开发一个人机协同决策系统。

英文摘要：

Rehabilitation assessment is critical to determine an adequate intervention for a patient. However, the current practices of assessment mainly rely on therapist's experience, and assessment is infrequently executed due to the limited availability of a therapist. In this paper, we identified the needs of therapists to assess patient's functional abilities (e.g. alternative perspective on assessment with quantitative information on patient's exercise motions). As a result, we developed an intelligent decision support system that can identify salient features of assessment using reinforcement learning to assess the quality of motion and summarize patient specific analysis. We evaluated this system with seven therapists using the dataset from 15 patient performing three exercises. The evaluation demonstrates that our system is preferred over a traditional system without analysis while presenting more useful information and significantly increasing the agreement over therapists' evaluation from 0.6600 to 0.7108 F1-scores ($p <0.05$). We discuss the importance of presenting contextually relevant and salient information and adaptation to develop a human and machine collaborative decision making system.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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