SCH: INT: Collaborative Research: Learning and Improving Alzheimer's Patient-Caregiver Relationships via Smart Healthcare Technology

超过80%的阿尔茨海默病或相关痴呆症患者在家中由家庭成员照顾。家庭照顾者经常报告焦虑和抑郁增加，许多人放弃了自己的健康需求，因为作为家庭照顾者的需求持续了很多年。众所周知，患者和护理人员之间的不良互动增加了提供护理的难度。监测患者和护理人员之间的反应可以在有问题的互动可能发生时发出信号。在这些时刻，及时甚至预测性的建议都可以改善这些互动，减少护理人员的压力。该项目开发了一种监测、建模和交互式推荐解决方案（针对护理人员），用于家庭痴呆症患者护理，重点关注护理人员与患者的关系。这包括监测情绪和压力，并分析监测这些属性对痴呆症患者护理的重要性，以及患者和护理者之间随后的行为动态。此外，在适当的时候，新颖和适应性的行为建议旨在帮助改善与护理相关的家庭互动，随着时间的推移，这将改善患者疾病的压力效应，减少照顾者的压力。这项工作还可以通过改善对居民的护理，使养老院和辅助生活机构受益，并可能对其他护理情况有用，包括照顾由家人在家照顾的有情感/行为挑战的儿童。教育模块向医疗保健专业的学生和技术专业的学生介绍这个多学科领域的研究。该技术解决方案包括一组基于统计学习的核心技术，用于自动生成家庭痴呆症患者护理所需的专业模块。个性化、背景和痴呆阶段都有助于对专门模块的需求；如果没有快速自动生成这些专业模块的新解决方案，有效治疗和改善患者/护理者关系的进展将非常困难和缓慢。解决方案中有三个主要的技术组件。第一种方法是从语音中获取文本内容和韵律，并使用机器学习技术创建分类模型。这种方法不仅可以监测患者的行为，还可以监测护理人员的行为，并推断出他们相互作用的潜在动态，例如情绪和压力的变化。第二个是自动创建针对特定患者和痴呆症状况（例如痴呆症的不同阶段）量身定制的分类器和推理模块。三是自适应推荐系统，完成了家庭行为监测系统的闭环。主要的技术贡献是个性化智能互联健康平台的快速准确发展，以及降低医疗成本的潜力。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Over 80% of people with Alzheimer's disease or a related dementia are cared for in their home environments by family members. Family caregivers often report increased anxiety and depression, and many forego their own health needs as the demands of being a family caregiver are sustained over many years. It is also known that poor interactions between patient and caregiver increase the difficulty of providing care. Monitoring reactivity between patient and caregiver could signal when problematic interactions might occur. Just-in-time or even predictive recommendations in those moments could improve these interactions and reduce strain on caregivers. This project develops a monitoring, modeling, and interactive recommendation solution (for caregivers) for in-home dementia patient care that focuses on caregiver-patient relationships. This includes monitoring for mood and stress and analyzing the significance of monitoring those attributes to dementia patient care and subsequent behavior dynamics between patient and caregiver. In addition, novel and adaptive behavioral suggestions at the right moments aim at helping improve familial interactions related to caregiving, which over time should ameliorate the stressful effects of the patient's illness and decrease strain on caregivers. This work could also benefit nursing homes and assisted living facilities by improving care for their residents, and could be useful for other caregiving situations, including the care of children with emotional/behavioral challenges who are cared for at home by their families. Educational modules introduce both healthcare students and technology students to this multidisciplinary area of research.The technical solution consists of a core set of statistical learning based techniques for automated generation of specialized modules required by in-home dementia patient care. Personalization, context, and stages of dementia all contribute to the need for specialized modules; and without new solutions for rapid and automatic generation of these specialized modules, progress in effective treatment and patient/caregiver relationship improvement will be very difficult and slow. There are three main technical components in the solution. The first obtains textual content and prosody from voice and uses machine learning techniques to create classification models. This approach not only monitors patients' behavior, but also caregivers', and infers the underlying dynamics of their interactions, such as changes in mood and stress. The second is the automated creation of classifiers and inference modules tailored to the particular patients and dementia conditions (such as different stages of dementia). The third is an adaptive recommendation system that closes the loop of an in-home behavior monitoring system. The main technical contribution is the quick and accurate development of personalized smart and connected health platforms and the potential for reduced medical costs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.