EHR-based vs population-based CVD risk predictions for older patients with diabetes

我们的应用程序名为“使用电子健康对代表性不足的人群进行公平的风险预测” 记录“回应NOT-OD-21-094：支持协作的行政补充，以改善 人工智能/机器学习[AI/ML]-NIH支持的数据的就绪性“，并补充了我们的 家长NIA R01基金(R01 AG065330)，题为《基于EHR与基于人群的老年人心血管风险预测》 糖尿病患者“。家长资助的首要目标是发展个性化的绝对风险。 使用EHR预测老年糖尿病患者的心血管疾病。我们建议 补充研究，以1)调查对种族/族裔少数群体的陈述不足(偏见)和 EHR中有弱势社会健康决定因素(SDOHs)的患者，2)发展公平意识的EHR 3)将模拟的EHR数据集、链接的SDOH数据集与开发的 公平风险预测工具，以激励和支持AI/ML研究社区进一步调查公平 EHR算法。在我们的母公司R01项目的实施过程中，我们发现了新出现的证据- EHR对种族/民族少数群体和弱势SDOH患者的抽样偏差。这样的病人是 更有可能去多个机构接受护理，并且经常接受较少的诊断测试和 单一机构的电子病历数据中的药物。我们假设少数族裔和病人 对于处境不利的SDOH，样本量较小、诊断不足和 实验室信息，以及在电子病历中较少遇到的情况。因此，我们假设基于电子病历的 风险预测模型(包括传统的线性模型和现代AI/ML方法)忽略了 不平衡的抽样将对这些代表性不足的患者群体做出较不准确的预测。 在系统调查这些偏见的影响方面，很少或根本没有做任何工作。然后，我们提议 为企业人力资源制定公平改善预测方法。在补充项目完成后， 制定的公平预测将为更广泛的AI/ML研究奠定基础并提供资源 开发公平预测以推进对种族/民族的疾病预测和检测的社区 少数族裔和弱势SDOHs患者。

Our application entitled “Fair Risk Predictions for Underrepresented Populations using Electronic Health Records” responds to NOT-OD-21-094: “Administrative Supplements to Support Collaborations to Improve the Artificial Intelligence/Machine Learning [AI/ML]-Readiness of NIH-Supported Data”, and supplements our parent NIA R01 grant (R01 AG065330) entitled “EHR-based vs population-based CVD risk predictions for older patients with diabetes”. The overarching goal of the parent grant is to develop individualized absolute risk predictions of Cardiovascular diseases for older patients with diabetes using EHRs. We propose the supplement study, in parallel, to 1) investigate under-representations (bias) of racial/ethnic minorities and patients with disadvantaged Social Determinants of Health (SDOHs) in EHRs, 2) develop fairness-aware EHR prediction methods; and 3) share the simulated EHR datasets, linked SDOH datasets along with the developed fair risk prediction tools to inspire and enable the AI/ML research community for further investigations of fair EHR algorithms. During the implementation of our parent R01 project, we found emerging evidence of under- sampling bias in EHRs for racial/ethnic minorities and patients with disadvantaged SDOHs. Such patients are more likely to visit multiple institutions to receive care, and often receive fewer diagnostic tests and medications in the EHR data of a single institution. We hypothesize that racial/ethnic minorities and patients with disadvantaged SDOHs are under-represented with smaller sample sizes, insufficient diagnostics and laboratory information, and less frequent encounters in EHRs. Consequently, we hypothesize that EHR-based risk prediction models (including conventional linear models and modern AI/ML methods) ignoring the unbalanced samplings will have less-accurate predictions for these under-represented patient populations. Little or no work has been done to systematically investigate the impact of these biases. We then propose to develop fairness improvement prediction approaches for EHRs. Upon the supplement project completion, the developed fair predictions will lay the groundwork and provide resources for the broader AI/ML research community for developing fair predictions to advance disease predictions and detections for racial/ethnic minorities and patients with disadvantaged SDOHs.