NOT-OD-23-070: Empowering Cloud Computing for Non-image-based Diabetic Retinopathy Screening by Designing an EHR-oriented Incremental Learning Framework

项目摘要/摘要 尽管糖尿病视网膜病变(DR)的发病率很高，但推荐的年度眼科检查 糖尿病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 医生注意，因为DR早期没有症状，因此错过了最有效的时期 阻止DR进展，防止视力丧失。此外，DR检查的眼科设备主要是 仅限于城市地区，限制收入有限的农村社区的患者进入。所有这些都是 迫切需要开发创新的方法，使之能够及早发现DR。 我们的长期目标是开发一种经济高效的、非基于图像的人工智能(AI)工具，用于小学 护理医生使用共病数据和常规实验室结果来评估患者患DR的风险，这些数据和常规实验室结果被广泛使用 可用。它将帮助医生推荐眼科检查和高危个体筛查频率。 患者信心十足。我们的母公司NEI项目的目标是提高数据质量和预测精度 利用张量信息进行DR筛查。母项目演示了检测 DR的存在准确率约为92%。我们的方法有望提高 在渐近患者中推荐眼科检查，打破无处不在的糖尿病眼部护理的障碍 在农村社区，并使成千上万的人免于失明。 在父项目中开发的深度学习模型使用Cerner Real-World Data中的数据进行训练 (CRWD)。CRWD是一个全国性的、全面的关系数据库，包含真实世界、未识别、HIPAA- 合规的患者数据，包括超过1亿名患者和来自不同护理环境的15亿次接诊。 从2023年开始，Cerner将CRWD完全转移到亚马逊网络服务(AWS)。CENER更新 CRWD每季度一次。更新的数据带来了持续改进预测模型的机会，但 此外，在云上使用海量数据重新训练模型的计算负担也很高。 这个补充项目的技术目标是设计一种创新的样品回收辅助 增量学习(SR-IL)框架，用于使用新添加的EHR数据更新深度神经网络模型， 而不需要完全重新训练模型。我们将在AWS上实施建议的SR-IL框架 不断改进我们的非基于图像的DR筛查的预测模型。 如今，越来越多的电子健康记录(EHR)数据被转移到云端。然而，没有 面向电子病历的增量学习框架减少更新带来的计算负担 医疗保健分析在云上建立模型。从这项研究制定的方法论将有助于 提高医疗保健分析的效率，并有助于持续改善 其他疾病的预测模型，在不增加高计算负担的情况下，完全重新训练深度- 学习云上海量数据的模型。

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 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 create an urgent need to develop innovative approaches that enable early detection of DR. 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 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 aim of our parent NEI project is to improve data quality and prediction accuracy for DR screening by harnessing tensor information. The parent project demonstrated the feasibility of detecting the existence of DR with about 92% 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. The deep-learning models developed in the parent project were trained with data from Cerner Real-World Data (CRWD). CRWD is a nation-wide, comprehensive, relational database of real-world, de-identified, HIPAA- compliant patient data with over 100 million patients and 1.5 billion encounters from diverse care settings. Starting from 2023, Cerner moved CRWD to Amazon Web Services (AWS) completely. Cerner updates CRWD every quarter. The updated data brings opportunities to continuously improve the predictive models, but also the high computational burden to re-train the models with huge amount of data on the cloud. The technical objective of this supplemental project is to design an innovative sample recycling-assisted incremental learning (SR-IL) framework to update deep neural network models with newly added EHR data, without requiring completely re-training the models. We will implement the proposed SR-IL framework on AWS to continuously improve our predictive models for non-image-based DR screening. More and more Electronic Health Record (EHR) data are being moved to the cloud today. However, there is no EHR-oriented incremental learning framework to reduce the computational burden caused by updating healthcare analytics models on the cloud. The methodology developed from this study will contribute to increasing the efficiency of healthcare analytics and help continuously improve the performance of the predictive models of other diseases without adding high computational burden to completely re-train the deep- learning models with huge amount of data on the cloud.