SCC-PG: Just in Time Intervention for Patients with Chronic Heart Diseases in Arizona tribes

SCC-PG：对亚利桑那州部落慢性心脏病患者进行及时干预

• 批准号: 2125643
• 负责人: Fatemeh Afghah
• 金额: $ 15万
• 依托单位: Northern Arizona University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2125643&HistoricalAwards=false
• 关键词: SCC; PG; Just; Time; Intervention

Cardiovascular diseases are the leading cause of death in the United States. Cardiovascular diseases are often chronic conditions that involve several costly emergency visits or long hospitalizations for the patients. Limited access to medical facilities in rural communities can result in worse health outcomes for these patients, in particular, the American Indian (AI) patients living in remote and rural areas. A considerable number of Arizonan AIs with cardiovascular conditions may be at risk of missing the window of opportunity for effective treatment and experiencing a lower chance of survival because of living far away from medical service providers. Arizona has the third largest population of Indian Americans who live in rural, tribal and often extremely isolated areas. The cardiac patients living in these areas do not have the required timely access to care, in particular to specialty services like cardiologists. Therefore, an important challenge related to these conditions for rural patients is the ‘early detection of deterioration in symptoms’, which is critical for ‘just in time’ interventions. This planning project is a collaborative effort among the Northern Arizona University (NAU) and University of Michigan (UM) as well as community leaders in rural and tribal health to discuss the best strategies to utilize an integrated remote heart monitoring system to benefit the patients with chronic heart conditions who live in rural, remote and isolated tribal areas.This planning project offers several innovative approaches by working with the tribal AI community to begin(i) Developing a new remote heart monitoring technology to predict the deterioration of the symptoms and occurrence of critical heart conditions in patients with some common chronic cardiac conditions such as atrial fibrillation and congested heart failure. Several remote heart monitoring systems have focused on reliable detection of such events, however by the time that the device alerts the patients or their family, it is already too late to seek medical help for the patients who live in rural and remote areas. Hence, our early prediction framework can give the patients enough time to seek medical assistance. This system can also help caregivers control severe symptoms, reduce readmissions, and reduce the cost associated with care; (ii) Developing deep learning-based and Markov-based prediction methods; and (iii) Developing on-device prediction methods that can work independently of the cloud in order to service the patients with no access to broadband internet. This study can be replicated for a wide range of other diseases and medical conditions and in different geographic regions.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.

心血管疾病是美国的主要原因，心血管疾病通常是慢性疾病，患者需要多次昂贵的紧急就诊或长期住院，而农村社区的医疗设施有限可能会导致这些患者的健康状况恶化。特别是生活在偏远和农村地区的美洲印第安人（AI）患者，相当多的患有心血管疾病的亚利桑那州患者可能会因为居住距离太远而面临错过有效治疗机会和生存机会较低的风险。远离医疗服务提供者的亚利桑那州拥有第三个。居住在农村、部落和往往极其偏远地区的印裔美国人人口最多，生活在这些地区的心脏病患者无法及时获得所需的护理，特别是心脏病专家等专业服务，因此，与这些相关的一个重要挑战。农村患者的健康状况的关键是“及早发现症状恶化”，这对于“及时”干预至关重要。该规划项目是北亚利桑那大学 (NAU) 和密歇根大学 (UM) 的合作成果。作为农村和部落卫生领域的社区领袖，讨论利用集成远程心脏监测系统使生活在农村、偏远和孤立部落地区的慢性心脏病患者受益的最佳策略。该规划项目通过与部落人工智能社区合作，提供了几种创新方法，以开始(i)开发新的远程心脏监测技术可预测患有某些常见慢性心脏病（例如心房颤动和充血性心力衰竭）的患者的症状恶化和危重心脏疾病的发生，然而，一些远程心脏监测系统专注于可靠检测此类事件。当设备发出警报时对于居住在农村和偏远地区的患者或他们的家人来说，寻求医疗帮助已经太晚了。因此，我们的早期预测框架可以让患者有足够的时间寻求医疗帮助，该系统也可以帮助护理人员进行控制。严重症状，减少再入院，并降低与护理相关的成本；(ii) 开发基于深度学习和基于马尔可夫的预测方法；(iii) 开发可以独立于云运行的设备上预测方法以提供服务；无法接入宽带互联网的患者。这项研究可以在不同地理区域针对多种其他疾病和医疗状况进行复制。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力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

Arrhythmia Classification Using CGAN-Augmented ECG Signals

使用 CGAN 增强心电图信号进行心律失常分类

• DOI: 10.1109/bibm55620.2022.9995088
• 发表时间: 2022-12
• 期刊: IEEE
• 作者: Adib, Edmond;Afghah, Fatemeh;Prevost, John J.
• 通讯作者: Prevost, John J.