I-Corps: Artificial Intelligence Models to Improve Heart Failure Management

I-Corps：改善心力衰竭管理的人工智能模型

• 批准号: 2234141
• 负责人: Roman Lubynsky
• 金额: $ 5万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2234141&HistoricalAwards=false
• 关键词: Corps; Artificial; Intelligence; Models; Improve

The broader impact/commercial potential of this I-Corps project is the development of technology that could improve heart failure management for many stakeholders, including hospitals, integrated health organizations (IHOs), medical insurers, electronic health records (EHRs) vendors, medical device manufacturers, pharmaceutical companies, and contract research organizations (CROs). Hospitals, IHOs and medical insurers may see value in the technology’s potential to improve patient outcomes, increase productive lifespans, and reduce healthcare costs. Pharmaceutical companies, medical device manufacturers, and CROs may also see value in the technology’s potential to expedite and improve patient recruitment for clinical trials. Furthermore, the technology could ultimately be applied to adjacent chronic conditions, such as chronic obstructive pulmonary disease (COPD) and kidney failure. The vision is to develop an artificial intelligence (AI) platform that integrates siloed patient data to improve management of chronic conditions.This I-Corps project is based on the development of machine learning (ML) algorithms that stratify and phenotype heart failure patients to identify those at risk of adverse events and recommend appropriate interventions. The ML models are trained on millions of de-identified patient records including chest radiographs and numerical data (e.g., vital signs and lab tests). The trained artificial intelligence (AI) models identify subtle indicators and patterns from the clinical data and stratify patients on a granular level. The deep phenotyping has the potential to personalize intervention plans for heart failure management. The team developed machine learning (ML) algorithms to quantitatively assess patient fluid overload from chest radiographs, finding chest radiographs contain information that can be used to predict readmissions of heart failure patients. The 30-day readmission rate correlates well with the pulmonary edema severity that the AI model estimates based on the pre-discharge chest x-ray images.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.

该 I-Corps 项目更广泛的影响/商业潜力是开发可以改善许多利益相关者心力衰竭管理的技术，包括医院、综合健康组织 (IHO)、医疗保险公司、电子健康记录 (EHR) 供应商、医疗设备制造商、制药公司和合同研究组织 (CRO) 医院、IHO 和医疗保险公司可能会看到该技术在改善患者治疗效果、延长生产寿命和降低医疗保健成本方面的潜力。 CRO 还可能看到该技术在加速和改善临床试验患者招募方面的价值，此外，该技术最终可以应用于慢性阻塞性肺疾病 (COPD) 和肾衰竭等邻近疾病。一个人工智能 (AI) 平台，集成孤立的患者数据，以改善慢性病的管理。这个 I-Corps 项目基于机器学习 (ML) 算法的开发，该算法对心力衰竭患者进行分层和表型分析，以识别那些有不良风险的患者事件和机器学习模型经过数以百万计的去识别化患者记录的训练，包括胸部X光照片和数字数据（例如生命体征和实验室测试），经过训练的人工智能（AI）模型可以从临床数据中识别微妙的指标和模式。深度表型分析有可能为心力衰竭管理制定个性化的干预计划，该团队开发了定量机器学习（ML）来评估患者胸片中的液体超负荷，发现胸片包含信息。可用于预测心力衰竭患者的再入院率。AI 模型根据出院前胸部 X 光图像估计的肺水肿严重程度与 30 天的再入院率密切相关。该奖项反映了 NSF 的法定使命。通过使用基金会的智力优点和更广泛的影响审查标准进行评估，并被认为值得支持。