Telehealth helps to facilitate access to medical professionals by enabling remote medical services for the patients. These services have become gradually popular over the years with the advent of necessary technological infrastructure. The benefits of telehealth have been even more apparent since the beginning of the COVID-19 crisis, as people have become less inclined to visit doctors in person during the pandemic. In this paper, we focus on facilitating the chat sessions between a doctor and a patient. We note that the quality and efficiency of the chat experience can be critical as the demand for telehealth services increases. Accordingly, we develop a smart auto-response generation mechanism for medical conversations that helps doctors respond to consultation requests efficiently, particularly during busy sessions. We explore over 900,000 anonymous, historical online messages between doctors and patients collected over nine months. We implement clustering algorithms to identify the most frequent responses by doctors and manually label the data accordingly. We then train machine learning algorithms using this preprocessed data to generate the responses. The considered algorithm has two steps: a filtering (i.e., triggering) model to filter out infeasible patient messages and a response generator to suggest the top-3 doctor responses for the ones that successfully pass the triggering phase. The method provides an accuracy of 83.28\% for precision@3 and shows robustness to its parameters.
翻译:远程保健有助于通过为病人提供远程医疗服务来方便医疗专业人员的就诊。随着必要的技术基础设施的出现,这些服务多年来逐渐受到欢迎。自COVID-19危机开始以来,远程保健的好处更加明显,因为人们在大流行病期间不太愿意亲自看医生。在本文中,我们侧重于便利医生和病人之间的聊天会话。我们注意到,随着对远程保健服务需求的增加,聊天经验的质量和效率可能至关重要。因此,我们为医疗谈话开发了一个智能自动反应生成机制,帮助医生对咨询请求作出有效反应,特别是在繁忙的会议期间。我们探索了超过900 000个匿名、历史性的在线信息,在医生和病人之间收集了九个多月的时间。我们采用分组算法,以确定医生最经常作出的反应,并据此对数据进行手工标签。我们随后用这种预处理的数据来培训机器学习算法,以产生反应。我们考虑的算法有两个步骤:过滤(即触发)模式,以过滤不实用的病人信息,以及反应发电机建议上-3级医生对正确度进行精确度测试的方法。