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: 1838615
• 负责人: Tariq Iqbal
• 金额: $ 73.54万
• 依托单位: University of Virginia Main Campus
• 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: 2019-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1838615&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家庭环境中由家庭成员照顾。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，因为多年来一直要求成为家庭照顾者。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能会在可能发生问题的相互作用时发出信号。在这些时刻及时甚至预测性的建议可以改善这些互动，并减少对护理人员的压力。该项目开发了一个监测，建模和交互式推荐解决方案（为照顾者）的家庭痴呆症患者护理，重点是患者与患者的关系。这包括监测情绪和压力，并分析监测这些属性对痴呆患者护理的重要性，以及患者和护理人员之间的后续行为动态。此外，在适当的时候提出新颖和适应性的行为建议，旨在帮助改善与照顾有关的家庭互动，随着时间的推移，这应该会减轻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护理来使养老院和辅助生活设施受益，并且可以用于其他护理情况，包括照顾有情绪/行为挑战的儿童，这些儿童在家中由家人照顾。教育模块向医疗专业学生和技术专业学生介绍了这一多学科研究领域。技术解决方案包括一套基于统计学习的核心技术，用于自动生成家庭痴呆患者护理所需的专业模块。个性化，背景和痴呆症的阶段都有助于对专业模块的需求;如果没有快速自动生成这些专业模块的新解决方案，有效治疗和患者/护理人员关系改善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，也监测患者的行为，并推断他们相互作用的潜在动力，如情绪和压力的变化。第二个是自动创建分类器和推理模块，为特定的患者和痴呆症状况（如痴呆症的不同阶段）量身定制。第三个是自适应推荐系统，它关闭了家庭行为监控系统的循环。该奖项反映了NSF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。

项目成果

Comparative Explanations of Recommendations

• DOI: 10.1145/3485447.3512031
• 发表时间: 2021-11
• 期刊: Proceedings of the ACM Web Conference 2022
• 作者: Aobo Yang;Nan Wang;Renqin Cai;Hongbo Deng;Hongning Wang

Explanation as a Defense of Recommendation

• DOI: 10.1145/3437963.3441726
• 发表时间: 2021-01
• 期刊: Proceedings of the 14th ACM International Conference on Web Search and Data Mining
• 作者: Aobo Yang;Nan Wang;Hongbo Deng;Hongning Wang

When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution

• DOI: 10.1145/3404835.3462852
• 发表时间: 2021-04
• 期刊: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval
• 作者: Chuanhao Li;Qingyun Wu;Hongning Wang

Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits

• 发表时间: 2021-10
• 期刊: ArXiv
• 作者: Chuanhao Li;Hongning Wang

When Are Linear Stochastic Bandits Attackable?

线性随机强盗何时会受到攻击？

• 发表时间: 2022
• 期刊: Proceedings of the 39th International Conference on Machine Learning
• 作者: Wang, Huazheng;Xu, Haifeng;Wang, Hongning
• 通讯作者: Wang, Hongning