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 th...