Martinho Diogo, Freitas Alberto, Sá-Sousa Ana, Vieira Ana, Meira Jorge, Martins Constantino, Marreiros Goreti
Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD), Institute of Engineering, Polytechnic of Porto, Porto, Portugal.
CINTESIS - Center for Health Technology and Services Research, Porto, Portugal.
J Med Syst. 2021 Jan 30;45(3):31. doi: 10.1007/s10916-020-01704-5.
Over the last decades, an increase in the ageing population and age-related diseases has been observed, with the increase in healthcare costs. As so, new solutions to provide more efficient and affordable support to this group of patients are needed. Such solutions should never discard the user and instead should focus on promoting more healthy lifestyles and provide tools for patients' active participation in the treatment and management of their diseases. In this concern, the Personal Health Empowerment (PHE) project presented in this paper aims to empower patients to monitor and improve their health, using personal data and technology assisted coaching. The work described in this paper focuses on defining an approach for user modelling on patients with chronic obstructive respiratory diseases using a hybrid modelling approach to identify different groups of users. A classification model with 90.4% prediction accuracy was generated combining agglomerative hierarchical clustering and decision tree classification techniques. Furthermore, this model identified 5 clusters which describe characteristics of 5 different types of users according to 7 generated rules. With the modelling approach defined in this study, a personalized coaching solution will be built considering patients with different necessities and capabilities and adapting the support provided, enabling the recognition of early signs of exacerbations and objective self-monitoring and treatment of the disease. The novel factor of this approach resides in the possibility to integrate personalized coaching technologies adapted to each kind of user within a smartphone-based application resulting in a reliable and affordable alternative for patients to manage their disease.
在过去几十年中，人们观察到老龄人口增加以及与年龄相关的疾病增多，医疗保健成本也随之上升。因此，需要新的解决方案，为这类患者提供更高效且经济实惠的支持。此类解决方案绝不应抛弃用户，而应着重促进更健康的生活方式，并为患者积极参与疾病的治疗和管理提供工具。在这方面，本文介绍的个人健康赋能（PHE）项目旨在利用个人数据和技术辅助指导，使患者能够监测并改善自身健康。本文所述工作重点在于使用混合建模方法为慢性阻塞性呼吸道疾病患者定义一种用户建模方法，以识别不同的用户群体。结合凝聚层次聚类和决策树分类技术，生成了一个预测准确率为90.4%的分类模型。此外，该模型根据生成的7条规则识别出5个聚类，描述了5种不同类型用户的特征。利用本研究中定义的建模方法，将构建一个个性化指导解决方案，考虑到患者的不同需求和能力，调整所提供的支持，从而能够识别病情加重的早期迹象，并实现对疾病的客观自我监测和治疗。这种方法的新颖之处在于，有可能在基于智能手机的应用程序中集成适合各类用户的个性化指导技术，为患者管理疾病提供一种可靠且经济实惠的选择。
