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.

项目概要/摘要 尽管糖尿病视网膜病变 (DR) 的患病率很高，但建议每年对糖尿病视网膜病变进行眼科检查 患者的依从率非常低，只有 43% 左右，许多患者没有寻求适当的医疗护理。 由于DR早期无症状，从而错过了阻止DR的最有效时期 此外，用于 DR 检查的眼科设备主要限于 所有这些问题都造成了城市地区收入有限的患者的访问。 迫切需要具有成本效益、广泛可用的方法来实现早期发现 DR。 我们的长期目标是为初级保健医生开发一种非基于图像的人工智能 (AI) 工具 使用广泛可用的合并症数据和常规实验室结果来评估患者患 DR 的风险将会有所帮助。 医生自信地为高危患者推荐眼科检查和个人筛查频率。 我们的方法的准确性接近基于眼底图像的 DR 检测工具，并且更容易 初步研究证明了以 90% 的准确率检测 DR 的可行性。 我们的方法有望提高推荐眼科检查的合规率 无症状患者，打破农村社区普遍存在的糖尿病眼部护理障碍，拯救数千人的生命 如果成功的话，我们的方法有可能改变未来的灾难恢复护理。 它将识别 DR 的致病因素和临床上可改变的因素，这将导致积极主动的 DR。 预防和管理工具，以减少可避免的灾难恢复并支付医疗费用。 作为追求长期目标的下一步，我们将开发灾难恢复预测模型并提取训练 来自 Cerner Health Facts 的数据，这是一个真实世界、去识别化、HIPAA 的综合关系数据库 然而，与其他电子健康记录 (EHR) 数据库类似，其质量受到影响。 来自缺失值、不平衡和未标记的数据 此外，EHR 数据是多维的。 为了应对技术挑战，它们通常在两个视图特征（纵向或横截面）中进行检查。 因此，高阶统计数据（相关信息）在医疗保健分析中没有得到很好的利用。 张量信息对于优化医疗决策非常重要，并提供了一个独特的角度来解决 数据缺失、不平衡或未标记的问题。 不仅取决于患者当​​前的健康状况，还取决于他或她的病史。 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 该方法是通过同时处理纵向信息来实现的。 将有助于提高 EHR 数据的质量以及多种疾病预测模型的准确性。 项目总结/摘要第 6 页 联系人 PD/PI：刘铁鸣 叙事 尽管糖尿病视网膜病变（DR）是美国成年人失明的主要原因， 许多糖尿病患者不遵守推荐的眼科检查，因为 DR 早期无症状，患者错过了停止DR的最有效时期 进展并防止视力丧失。 眼科检查并及早发现 DR，我们的长期目标是开发一种具有成本效益的、非 基于图像的人工智能 (AI) 工具，供初级保健医生评估患者的风险 DR 使用常规实验室结果，并推荐眼科检查和个性化筛查 作为实现这一目标的下一步，该项目的目标是充满信心地为高危患者提供服务。 开发先进的机器学习算法，以充分发挥电子健康的潜力 通过利用张量信息来提高 EHR 数据的质量和记录 (EHR) 数据 预测准确性。