SCH: Harnessing Tensor Information to Improve EHR Data Quality for Accurate Data-driven Screening of Diabetic Retinopathy with Routine Lab Results

SCH：利用张量信息提高 EHR 数据质量，通过常规实验室结果进行数据驱动的糖尿病视网膜病变的准确筛查

• 批准号: 10491247
• 负责人: Tieming Liu
• 金额: $ 28.95万
• 依托单位: OKLAHOMA STATE UNIVERSITY STILLWATER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-30 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10491247
• 关键词: Accounting; Address; Adult; American; Architecture; Artificial Intelligence; Bayesian Modeling; Blindness; Caring; Classification; Clinical; Complex; Data; Data Set; Databases; Decision Making; Detection; Diabetic Retinopathy; Diagnosis; Dimensions; Disease; Disease Progression; Early Diagnosis; Electronic Health Record; Equipment; Eye; Frequencies; Future; Gaussian model; Goals; Health; Health Care Costs; Health Insurance Portability and Accountability Act; Healthcare; High Prevalence; Income; Individual; Learning; Machine Learning; Measures; Medical; Medical History; Methodology; Methods; Minority; Modeling; Network-based; Neurons; Patient risk; Patients; Pattern; Persons; Physicians; Prevention; Primary Care Physician; Process; Risk; Rural Community; Techniques; Time; Training; Treatment outcome; Uncertainty; base; comorbidity; convolutional neural network; cost effective; data quality; data structure; deep learning; deep neural network; design; diabetic; diabetic patient; electronic structure; fundus imaging; generative adversarial network; improved; innovation; longitudinal analysis; loss of function; machine learning algorithm; medical attention; multidimensional data; network architecture; novel; personalized screening; predictive modeling; prevent; relational database; screening; secondary analysis; statistics; tool; urban area

Project Summary / Abstract Despite the high prevalence of diabetic retinopathy (DR), the recommended annual ophthalmic exam for diabetic patients has a very low compliance rate, only around 43%. Many patients do not seek proper medical attention because DR is asymptomatic in the early stage, and thus they miss the most effective period to halt DR progression and prevent vision loss. Moreover, ophthalmic equipment for DR exams is predominantly limited to urban areas, restricting access by patients in rural communities with limited incomes. All of these issues create an urgent need for cost-effective, widely-available approaches that enable early detection of DR. Our long-term goal is to develop a non-image-based, artificial intelligence (AI) tool for primary care physicians to assess patients' risk for DR using comorbidity data and routine lab results, which are widely available. It will help physicians recommend ophthalmic exams and individual screening frequency for at-risk patients confidently. The accuracy of our approach is close to the fundus-image-based DR detection tools, and it is much easier to use and more cost-effective. Preliminary studies demonstrated the feasibility of detecting DR with 90% accuracy. Our approach is promising to increase the compliance rate of the recommended ophthalmic exams among asymptotic patients, break the barrier to ubiquitous diabetic eye care in rural communities, and save thousands of people from blindness. If successful, our approach has the potential to transform future DR care from reactive to proactive. It will identify the causative and clinically modifiable factors of DR. This will lead to a proactive DR prevention and management tool to reduce avoidable DR and defray healthcare costs. As the next step in pursuing our long-term goal, we will develop predictive models for DR and extract training data from Cerner Health Facts, a comprehensive, relational database of real-world, de-identified, HIPAA- compliant patient data. However, similar to other electronic-health-record (EHR) databases, its quality suffers from missing values, imbalanced and unlabeled data. In addition, although EHR data are multi-dimensional, due to technical challenges, they are often examined in two-view features (either longitudinal or cross-sectional). Thus the high order statistics (correlation information) are not well utilized in healthcare analytics. Tensor information is important to optimize medical decision making and provides a unique angle to address the problems of missing, imbalanced, or unlabeled data. The progression of a disease or the outcome of treatment not only depends on the patient's current health conditions, but also his or her medical history. To realize the full potential of EHR data, this project will study novel imputation, augmentation, classification, and machine learning techniques by simultaneously handling the longitudinal information. The methodology developed from this study will help improve the quality of EHR data and the accuracy of the predictive models for a wide range of diseases. Project Summary/Abstract Page 6 Contact PD/PI: Liu, Tieming Narratives Although diabetic retinopathy (DR) is the leading cause of blindness among American adults, many diabetic patients do not comply with the recommended ophthalmic exams because DR is asymptomatic in the early stages, and thus patients miss the most effective period to halt DR progression and prevent vision loss. To improve the compliance rate of the recommended ophthalmic exams and detect DR early, our long-term goal is to develop a cost-effective, non- image based, artificial intelligence (AI) tool for primary care physicians to assess patients’ risk for DR using routine lab results, and recommend ophthalmic exams and personalized screening frequency for at-risk patients confidently. As the next step in pursuing this goal, this project aims to develop advanced machine learning algorithms to realize the full potential of electronic-health- record (EHR) data by harnessing tensor information to improve the quality of EHR data and prediction accuracy.

项目摘要 /摘要 尽管糖尿病性视网膜病的患病率很高（DR），但建议的年度眼科检查 患者的合规率非常低，仅约43％。许多患者没有寻求适当的医疗护理 因为DR在早期是无症状的，因此他们错过了最有效的时期 进展并防止视力丧失。此外，用于DR考试的眼科设备主要限于 城市地区，限制了收入有限的农村社区患者的通道。所有这些问题都创造了 迫切需要具有成本效益，广泛可用的方法，可以尽早发现DR。 我们的长期目标是为初级保健医生开发非图像的人工智能（AI）工具 使用合并症数据和常规实验室结果评估患者对DR的风险，这些结果广泛可用。它将有所帮助 医生建议对高危患者进行眼科检查和个人筛查频率。 我们方法的准确性与基于基础图像的DR检测工具接近，并且容易得多 使用和更具成本效益。初步研究表明，以90％精度检测DR的可行性。 我们的方法有望提高建议的眼科考试的合规率 渐近患者，打破农村社区无处不在的糖尿病眼保健的障碍，并节省数千个 来自失明的人。如果成功，我们的方法有可能从反应性转变未来的医生护理 积极主动。它将确定DR的病因和临床可修改因素。这将导致一个主动的博士 预防和管理工具可降低可避免的DR和支配医疗保健成本。 作为追求长期目标的下一步，我们将开发DR和提取培训的预测模型 来自Cerner Health Facts的数据，这是一个现实世界中的全面，关系数据库，已被识别，HIPAA- 合规的患者数据。但是，类似于其他电子保险箱（EHR）数据库类似，其质量受到了影响 由于缺失值，不平衡和未标记的数据。此外，尽管EHR数据是多维的，但 在技​​术挑战中，通常会以两种视图特征（纵向或横截面）对其进行检查。 高阶统计（相关信息）在医疗保健分析中没有很好地利用。 张量信息对于优化医疗决策很重要，并提供了一个独特的角度来解决 缺失，不平衡或未标记数据的问题。疾病的进展或治疗结果 不仅取决于患者当​​前的健康状况，还取决于他或她的病史。实现全部 EHR数据的潜力，该项目将研究新颖的插补，增强，分类和机器学习 通过简单地处理纵向信息来获得技术。这项研究开发的方法 将有助于提高EHR数据的质量以及多种疾病的预测模型的准确性。 项目摘要/摘要页面6 联系PD/PI：Liu，Tieming 叙述 尽管糖尿病性视网膜病（DR）是美国成年人失明的主要原因，但 许多糖尿病患者不符合建议的眼科检查，因为DR是 在早期阶段无症状，因此患者错过了最有效的时期，以阻止DR 进展并防止视力丧失。提高建议的合规率 眼科考试和早期检测DR，我们的长期目标是开发一种成本效益，非 - 基于图像的，人工智能（AI）工具，用于初级保健医生评估患者的风险 DR使用常规实验室结果，并推荐眼科考试和个性化筛查 高危患者的频率自信。作为追求这一目标的下一步，该项目的目标 开发先进的机器学习算法以实现电子健康的全部潜力 - 记录（EHR）数据通过利用张量信息来提高EHR数据的质量和 预测准确性。