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

项目摘要/摘要 尽管糖尿病视网膜病变(DR)的发病率很高，但建议对糖尿病患者进行年度眼科检查 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 因为DR在早期阶段没有症状，因此他们错过了阻止DR的最有效时期 促进进步，防止视力丧失。此外，用于DR检查的眼科设备主要限于 城市地区，限制收入有限的农村社区的患者就医。所有这些问题都产生了 迫切需要具有成本效益的、广泛可用的方法，使人们能够及早发现DR。 我们的长期目标是开发一种非基于图像的人工智能(AI)工具，供初级保健医生 使用广泛可用的共病数据和常规实验室结果来评估患者患DR的风险。这会有帮助的 医生自信地向高危患者推荐眼科检查和个人筛查频率。 该方法的准确率接近基于眼底图像的DR检测工具，并且更容易 使用方便，性价比更高。初步研究证明，检测糖尿病视网膜病变的准确率为90%。 我们的方法有望提高推荐的眼科检查的合规率 渐近患者，打破农村社区无处不在的糖尿病眼科护理的障碍，拯救数千人 失明的人。如果成功，我们的方法有可能将未来的DR护理从被动式转变为 变得积极主动。它将确定DR的致病因素和临床可修改因素，这将导致积极的DR 预防和管理工具，可减少可避免的灾难恢复并支付医疗成本。 作为追求长期目标的下一步，我们将为灾难恢复和提取训练开发预测模型 来自Cerner Health Fact的数据，这是一个全面的关系数据库，包含真实世界、未识别的HIPAA- 符合要求的患者数据。然而，与其他电子健康记录(EHR)数据库类似，其质量也受到影响 来自缺失值、不平衡和未标记的数据。此外，虽然电子病历数据是多维的，但由于 对于技术挑战，它们通常在两个视图特征(纵向或横截面)中进行检查。 因此，高阶统计量(相关信息)在医疗保健分析中没有得到很好的利用。 张量信息对优化医疗决策很重要，并提供了一个独特的角度来解决 数据丢失、不平衡或未标记的问题。疾病的进展或治疗的结果 不仅取决于患者目前的健康状况，还取决于他或她的病史。要充分实现 为了挖掘电子病历数据的潜力，该项目将研究新的补偿、增强、分类和机器学习 技术通过同时处理纵向信息。从这项研究发展而来的方法论 这将有助于提高电子病历数据的质量和对各种疾病的预测模型的准确性。 项目摘要/摘要第6页 联系PD/PI：刘铁明 叙事 虽然糖尿病视网膜病变(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是 早期无症状，因此患者错过了停止DR的最有效时期 促进进步，防止视力丧失。提高建议的合规率 眼科检查和早期发现DR，我们的长期目标是开发一种性价比高、 基于图像的人工智能(AI)工具，供初级保健医生评估患者患 DR使用常规实验室结果，并建议进行眼科检查和个性化筛查 自信地为高危患者提供频率。作为追求这一目标的下一步，该项目旨在 开发先进的机器学习算法，以实现电子健康的全部潜力- 通过利用张量信息来记录(EHR)数据以提高EHR数据的质量 预测的准确性。

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.