为远程医疗开发有效和自动化的患者参与度估计：一种机器学习方法
Developing an Effective and Automated Patient Engagement Estimator for Telehealth: A Machine Learning Approach
摘要：
我们讨论MET，一个基于学习的算法，提出了感知患者的参与程度在远程医疗会议。我们利用与心理学文献中经常使用的情感和认知特征相对应的潜在向量来了解一个人在半监督GAN框架中的参与程度。我们从心理健康的角度展示了这种方法的有效性，更具体地说，如何利用这种方法来更好地了解远程健康会话期间的患者参与。为了进一步开发可用于远程医疗的类似技术，我们还计划发布一个包含1299个视频片段的数据集MEDICA，每个视频片段长3秒，并展示相同的实验。我们的框架报告了40%的改进RMSE（均方根误差）超过国家的最先进的参与估计方法。在我们的真实世界测试中，我们还观察到心理治疗师报告的工作联盟量表得分之间存在正相关。这表明所提出的模型有潜力呈现与心理治疗师使用的参与度测量非常一致的患者参与度估计。
英文摘要：
We discuss MET, a learning-based algorithm proposed for perceiving a patient's level of engagement during telehealth sessions. We leverage latent vectors corresponding to Affective and Cognitive features frequently used in psychology literature to understand a person's level of engagement in a semi-supervised GAN-based framework. We showcase the efficacy of this method from the perspective of mental health and more specifically how this can be leveraged for a better understanding of patient engagement during telemental health sessions. To further the development of similar technologies that can be useful for telehealth, we also plan to release a dataset MEDICA containing 1299 video clips, each 3 seconds long and show experiments on the same. Our framework reports a 40% improvement in RMSE (Root Mean Squared Error) over state-of-the-art methods for engagement estimation. In our real-world tests, we also observed positive correlations between the working alliance inventory scores reported by psychotherapists. This indicates the potential of the proposed model to present patient engagement estimations that aligns well with the engagement measures used by psychotherapists.
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