The measurement of Blood Urea Nitrogen (BUN) is an important test for examining the kidney condition. BUN is a waste product that is eliminated from the body by the kidneys. Increased BUN level often indicates renal diseases. The invasive blood test is used as the standard BUN testing method. In this paper, we have demonstrated an effective and innovative method for noninvasive BUN prediction from Ten-second fingertip video. We use the Ten-second fingertip video of human subjects to create photo plethysmography (PPG) features through signal processing and feature engineering techniques. We utilize Generative Adversarial Networks (GAN), a type of Generative AI, as a data augmentation method to create synthetic data based on our original data. Then, we use the PPG features and Gold standard BUN level of subj ects to develop four regression models before and after the implementation of GAN. We compare all models' performance in terms of MAE, MSE, and RMSE. Before applying GAN, ANN outperforms other models with an MAE of 1.17, MSE of 2.62, and RMSE of 1.62. We find that GAN implementation significantly improves every model's performance and provides lower MAE, MSE, and RMSE. After applying GAN, ANN performs best among all other models and shows the lowest MAE of 0.69, MSE of 1.03, and RMSE of 1.01. With these outstanding results, our noninvasive method of BUN prediction has the potential to offer enhanced patient comfort, early detection, continuous and remote monitoring of renal health.

Next Step Clinic (NSC) is a neighborhood-based mental clinic in Milwaukee in the USA for early identification and intervention of Autism spectrum disorder (ASD) children. NSC's primary goal is to serve the underserved families in that area with children aged 15 months to 10 years who have ASD symptoms free of cost. Our proposed and implemented Electronic Medical Records (NSC: EMR) has been developed for NSC. This paper describes the NSC: EMR's design specification and whole development process with the workflow control of this system in NSC. This NSC: EMR has been used to record the patient’s medical data and make appointments both physically or virtually. The integration of standardized psychological evaluation form has reduced the paperwork and physical storage burden for the family navigator. By deploying the system, the family navigator can increase their productivity from the screening to all intervention processes to deal with ASD children. Even in the lockdown time, due to the pandemic of COVID-19, about 84 ASD patients from the deprived family at that area got registered and took intervention through this NSC: EMR. The usability and cost-effective feature has already shown the potential of NSC: EMR, and it will be scaled to serve a large population in the USA and beyond.