I-Corps: Postoperative Risk Prediction for Heart Failure Patients

I-Corps：心力衰竭患者的术后风险预测

• 批准号: 2230433
• 负责人: Dongping Du
• 金额: $ 5万
• 依托单位: Texas Tech University
• 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: 2022-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2230433&HistoricalAwards=false
• 关键词: Corps; Postoperative; Risk; Prediction; Heart

The broader impact/commercial potential of this I-Corps project is the development of a risk assessment platform embedded with novel machine learning algorithms to predict mortality and adverse events in heart failure patients after cardiac treatments and surgeries. The technology seeks to: 1) address uncertainty and data imbalance issues prevalent in healthcare data, 2) extract important features and biomarkers relevant to post-operative risks, and 3) develop new tools for ultrasound image analysis to predict the probability of adverse events. There is currently no established technology for healthcare professionals in the cardiac care field to analyze multiple data sources for risk assessment pre- and post-cardiac treatment. The proposed technology may help clinicians leverage advanced machine learning tools for treatment outcomes assessment and clinical decision-making. The technology may also promote the use of data science approaches and computer models in other clinical applications. The project seeks to enable accurate risk prediction and enhance confidence in post-operative treatment, improving the quality of cardiac care, and ultimately benefitting cardiac patients.This I-Corps project is based on the development of a software application to predict post-operative risk for end-stage heart failure patients after cardiac surgeries such as heart transplantation or left ventricular assist device (LVAD) implantation. The software application has a back-end server with novel machine learning algorithms trained using electronic health records (EHR) from previous heart transplant and LVAD recipients. Risk prediction is performed using customized machine learning algorithms and statistical risk models. Novel clustering techniques are used to identify groups of patients with similar characteristics, clinical variables of importance, as well as noise and outliers present in the data. The software also offers an easy-to-use interface that provides clients with clinically useful information and a visualization of risk factors linked to the risk prediction for an individual patient.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.

这个I-Corps项目更广泛的影响/商业潜力是开发一个嵌入新型机器学习算法的风险评估平台，以预测心脏治疗和手术后心力衰竭患者的死亡率和不良事件。该技术旨在：1）解决医疗保健数据中普遍存在的不确定性和数据不平衡问题，2）提取与术后风险相关的重要特征和生物标志物，3）开发用于超声图像分析的新工具，以预测不良事件的概率。目前，心脏护理领域的医疗保健专业人员还没有成熟的技术来分析多个数据源，以进行心脏治疗前后的风险评估。这项技术可以帮助临床医生利用先进的机器学习工具进行治疗结果评估和临床决策。该技术还可以促进数据科学方法和计算机模型在其他临床应用中的使用。I-Corps项目旨在为心脏移植或左心室辅助装置（LVAD）植入等心脏手术后的终末期心力衰竭患者开发一种预测术后风险的软件应用程序，旨在帮助患者准确预测术后风险，增强患者对术后治疗的信心，提高心脏护理质量，最终使心脏病患者受益。该软件应用程序有一个后端服务器，其中包含使用之前心脏移植和LVAD接受者的电子健康记录（EHR）训练的新型机器学习算法。风险预测使用定制的机器学习算法和统计风险模型进行。新的聚类技术用于识别具有相似特征的患者组、重要的临床变量以及数据中存在的噪声和离群值。该软件还提供了一个易于使用的界面，为客户提供临床有用的信息和可视化的风险因素与风险预测为个别病人。该奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的知识价值和更广泛的影响审查标准。