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

SCH：INT：合作研究：通过智能医疗技术学习和改善阿尔茨海默病患者与护理人员的关系

• 批准号: 2024588
• 负责人: Karen Rose
• 金额: $ 42.62万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2024588&HistoricalAwards=false
• 关键词: SCH; INT; Collaborative; Research; Learning

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.

超过80%的阿尔茨海默病或相关痴呆症患者在他们的家庭环境中由家人照顾。家庭照顾者经常报告说焦虑和抑郁增加，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多年。同样众所周知的是，患者和护理者之间的不良互动增加了提供护理的难度。监测患者和护理者之间的反应性可以发出信号，表明何时可能发生有问题的互动。在这些时刻及时甚至是预测性的建议可以改善这些互动，减轻照顾者的压力。该项目为居家痴呆症患者护理开发了一个监测、建模和交互式建议解决方案(针对照顾者)，重点关注照顾者与患者之间的关系。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以及患者和照顾者之间随后的行为动态的意义。此外，在适当的时候提出新颖的适应性行为建议，旨在帮助改善与照看有关的家庭互动，随着时间的推移，这应该会改善患者疾病的压力影响，并减少照顾者的压力。这项工作还可以通过改善对居民的照顾而使疗养院和辅助生活设施受益，并可用于其他照顾情况，包括在家中由家人照顾的有情绪/行为障碍的儿童的护理。教育模块将医疗保健专业的学生和技术专业的学生引入这一多学科的研究领域。技术解决方案包括一套基于统计学习的核心技术，用于自动生成居家痴呆患者护理所需的专门模块。痴呆症的个性化、背景和阶段都有助于对专门模块的需求；如果没有快速和自动生成这些专门模块的新解决方案，在有效治疗和改善患者/护理者关系方面的进展将非常困难和缓慢。该解决方案包含三个主要技术组件。第一种方法从语音中获取文本内容和韵律，并使用机器学习技术来创建分类模型。这种方法不仅监测患者的行为，也监测照顾者的行为，并推断他们互动的潜在动态，如情绪和压力的变化。第二种是根据特定患者和痴呆症情况(如痴呆症的不同阶段)自动创建分类器和推理模块。第三个是自适应推荐系统，它关闭了居家行为监控系统的循环。主要的技术贡献是快速、准确地开发个性化、智能和互联的健康平台，以及降低医疗成本的潜力。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。