I-Corps: Machine Learning Algorithm for Cardiovascular Disease Diagnosis

I-Corps：用于心血管疾病诊断的机器学习算法

• 批准号: 2331156
• 负责人: Tenderano Muzorewa
• 金额: $ 5万
• 依托单位: University of Memphis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2331156&HistoricalAwards=false
• 关键词: Corps; Machine; Learning; Algorithm; Cardiovascular

The broader impact/commercial potential of this I-Corps project is the development of a cardiovascular screening/diagnostic software tool that empowers clinicians to deliver higher quality care to all patient populations, including the historically underserved and those across the spectrum of cardiovascular disease risk. Cardiovascular disease misdiagnosis is as much as 50% higher in women than men The proposed software is designed to improve diagnostic accuracy for clinicians and provide better cardiovascular health outcomes for patients. Cardiovascular disease is the leading cause of death globally, and this software has the potential to reduce cardiovascular healthcare costs by reducing spending on unnecessary testing, procedures, and illness that occurs when patients are not screened early and diagnosed quickly and accurately. This technology focuses on those cardiovascular conditions that are misdiagnosed frequently and aims to improve health equity given the increased risk of misdiagnosis for women and the contribution of these conditions to the ongoing burden of cardiovascular health inequities that affect patients from minoritized genders and ethnicities.This I-Corps project is based on the development of a machine learning-based algorithm to assist clinicians in identifying which patients are at high risk of developing or worsening cardiovascular diseases. The proposed technology uses information in patients’ electronic health records, and the algorithm focuses on cardiovascular diseases that are not well understood, are often missed, and/or disproportionately affect women. The machine learning (ML) algorithm was trained and tested on the digital health records of a highly diverse group of patients and may more accurately provide cardiovascular disease (CVD) diagnoses for women and ethnic minorities than the current standard of care. Most ML tools for diagnosing CVD use deep learning to automate the interpretation of images and to interpret electrocardiogram (ECG) signals with similar or superior accuracy to specialist physicians. The training model used for this technology is designed to catch missed cases of CVD and is based on information that is commonly in patients’ electronic health records. This is because in many cases of misdiagnosis, CVD is not suspected by the clinician and CVD-specific tests/scans are not ordered or signals appear normal. The technology leverages physiological differences in patients and is developed with the aim of improving the accuracy of triaging patients and being able to identify patients with CVD earlier than with the existing rule-based systems.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项目的更广泛影响/商业潜力是开发心血管筛查/诊断软件工具，使临床医生能够为所有患者人群提供更高质量的护理，包括历史上服务不足的人群和心血管疾病风险范围内的人群。女性心血管疾病误诊率比男性高50%。该软件旨在提高临床医生的诊断准确性，并为患者提供更好的心血管健康结果。心血管疾病是全球死亡的主要原因，该软件有可能通过减少不必要的测试，程序和疾病的支出来降低心血管医疗保健成本，这些疾病是在患者没有得到早期筛查和快速准确诊断时发生的。 这项技术的重点是那些经常被误诊的心血管疾病，旨在提高健康公平性，因为妇女被误诊的风险增加，这些疾病对影响少数性别和种族患者的心血管健康不公平的持续负担的贡献。基于算法，以帮助临床医生确定哪些患者处于发展或恶化心血管疾病的高风险中。 这项技术使用了患者电子健康记录中的信息，该算法专注于不太了解、经常被遗漏和/或不成比例地影响女性的心血管疾病。 机器学习（ML）算法是在高度多样化的患者群体的数字健康记录上进行训练和测试的，与目前的护理标准相比，它可以更准确地为女性和少数民族提供心血管疾病（CVD）诊断。 大多数用于诊断CVD的机器学习工具使用深度学习来自动解释图像，并以与专科医生相似或上级的准确性解释心电图（ECG）信号。用于该技术的训练模型旨在捕捉错过的CVD病例，并基于患者电子健康记录中常见的信息。 这是因为在许多误诊病例中，临床医生没有怀疑CVD，并且没有安排CVD特定的测试/扫描或信号显示正常。该技术利用患者的生理差异，旨在提高患者分类的准确性，并能够比现有的基于规则的系统更早地识别CVD患者。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。