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

在美国，心血管疾病是主要的死亡原因。心血管疾病通常是慢性疾病，患者需要多次昂贵的急诊或长期住院。农村社区获得医疗设施的机会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国印第安人(AI)患者。相当数量的亚利桑那州有心血管疾病的人工智能可能面临错过有效治疗的机会窗口的风险，并且由于居住在远离医疗服务提供者的地方而经历较低的生存机会。亚利桑那州的印裔美国人人口位居第三，他们生活在农村、部落和往往极其孤立的地区。居住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科服务。因此，对农村患者来说，与这些情况相关的一个重要挑战是“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)以及农村和部落卫生社区领导人共同努力的结果，目的是讨论利用集成远程心脏监测系统造福居住在农村、偏远和与世隔绝的部落地区的慢性心脏病患者的最佳策略。该规划项目通过与部落人工智能社区合作，提供了几种创新的方法，以开始(I)开发一种新的远程心脏监测技术，以预测患有一些常见慢性心脏疾病的患者的症状恶化和严重心脏疾病的发生，如心房颤动和充血性心力衰竭。几个远程心脏监测系统的重点是可靠地检测到这类事件，然而，当设备向患者或他们的家人发出警报时，为生活在农村和偏远地区的患者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。该系统还可以帮助照顾者控制严重的症状，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马尔可夫的预测方法；以及(Iii)开发可独立于云工作的设备上预测方法，以便为无法接入宽带互联网的患者提供服务。这项研究可以在不同地理区域的广泛其他疾病和医疗条件下复制。这一奖项反映了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。

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.