Kadariya Dipesh, Venkataramanan Revathy, Yip Hong Yung, Kalra Maninder, Thirunarayanan Krishnaprasad, Sheth Amit
Kno.e.sis - Wright State University Dayton, USA.
Dayton Children's Hospital Dayton, USA.
Proc Int Conf Smart Comput SMARTCOMP. 2019 Jun;2019:138-143. doi: 10.1109/smartcomp.2019.00043. Epub 2019 Aug 1.
There is a well-recognized need for a shift to proactive asthma care given the impact asthma has on overall healthcare costs. The demand for continuous monitoring of patient's adherence to the medication care plan, assessment of environmental triggers, and management of asthma can be challenging in traditional clinical settings and taxing on clinical professionals. Recent years have seen a robust growth of general purpose conversational systems. However, they lack the capabilities to support applications such an individual's health, which requires the ability to contextualize, learn interactively, and provide the proper hyper-personalization needed to hold meaningful conversations. In this paper, we present kBot, a knowledge-enabled personalized chatbot system designed for health applications and adapted to help pediatric asthmatic patients (age 8 to 15) to better control their asthma. Its core functionalities include continuous monitoring of the patient's medication adherence and tracking of relevant health signals and environment data. kBot takes the form of an Android application with a frontend chat interface capable of conversing in both text and voice, and a backend cloud-based server application that handles data collection, processing, and dialogue management. It achieves contextualization by piecing together domain knowledge from online sources and inputs from our clinical partners. The personalization aspect is derived from patient answering questionnaires and day-to-day conversations. kBOT's preliminary evaluation focused on chatbot quality, technology acceptance, and system usability involved eight asthma clinicians and eight researchers. For both groups, kBot achieved an overall technology acceptance value of greater than 8 on the 11-point Likert scale and a mean System Usability Score (SUS) greater than 80.
鉴于哮喘对整体医疗成本的影响，向积极主动的哮喘护理模式转变的需求已得到广泛认可。在传统临床环境中，持续监测患者对药物护理计划的依从性、评估环境触发因素以及管理哮喘具有挑战性，且对临床专业人员来说负担较重。近年来，通用对话系统蓬勃发展。然而，它们缺乏支持诸如个人健康这类应用的能力，而这需要具备情境化、交互式学习以及进行有意义对话所需的适当超个性化的能力。在本文中，我们展示了kBot，这是一个基于知识的个性化聊天机器人系统，专为健康应用而设计，并经过调整以帮助8至15岁的小儿哮喘患者更好地控制他们的哮喘。其核心功能包括持续监测患者的药物依从性以及跟踪相关健康信号和环境数据。kBot采用安卓应用的形式，具有能够进行文本和语音对话的前端聊天界面，以及处理数据收集、处理和对话管理的基于云的后端服务器应用程序。它通过整合来自在线资源的领域知识和我们临床合作伙伴的输入来实现情境化。个性化方面则源自患者对问卷的回答以及日常对话。kBot的初步评估聚焦于聊天机器人质量、技术接受度和系统可用性，涉及八名哮喘临床医生和八名研究人员。对于这两组人员，kBot在11点李克特量表上的整体技术接受度值均大于8，平均系统可用性得分（SUS）大于80。
