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

项目总结/摘要 尽管糖尿病视网膜病变（DR）的患病率很高，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。许多患者不寻求适当的 由于DR在早期无症状，因此错过了最有效的时期， 阻止DR进展并防止视力丧失。此外，用于DR检查的眼科设备主要是 仅限于城市地区，限制了收入有限的农村社区患者的就医。所有这些 迫切需要开发能够早期发现DR的创新方法。 我们的长期目标是开发一个具有成本效益的，非图像为基础的，人工智能（AI）的工具，为小学 护理医生使用广泛使用的合并症数据和常规实验室结果来评估患者的DR风险 available.它将帮助医生推荐眼科检查和个人筛查频率的风险 患者信心十足。我们的母NEI项目的目的是提高数据质量和预测精度， 利用张量信息进行DR筛查。母项目证明了检测 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科护理的障碍 在农村社区，拯救了成千上万的人免于失明。 母项目中开发的深度学习模型使用Cerner Real-World Data的数据进行训练 （CRWD）。CRWD是一个全国性的，综合性的，真实世界的关系数据库，去识别，HIPAA- 合规的患者数据，超过1亿名患者和来自不同医疗环境的15亿次就诊。 从2023年开始，Cerner将CRWD完全迁移到Amazon Web Services（AWS）。Cerner更新 每季度CRWD。更新的数据带来了不断改进预测模型的机会， 此外，在云端重新训练具有大量数据的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个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上实现拟议的SR-IL框架 不断改进我们的非图像DR筛查预测模型。 如今，越来越多的电子健康记录（EHR）数据正在迁移到云端。但没有 面向EHR的增量学习框架，减少更新带来的计算负担 云上的医疗分析模型。从这项研究中得出的方法将有助于 提高医疗保健分析的效率，并帮助不断提高 其他疾病的预测模型，而不增加高计算负担，以完全重新训练深度， 学习模型和云上的大量数据。

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.