Significant health disparities exist in chronic kidney disease (CKD), CKD progression, and end stage renal disease (ESRD) in ethnically diverse populations. African Americans (AAs) have approximately 25% higher prevalence of CKD, and 3x higher rates of ESRD compared to their counterparts. Moreover, a portion of these individuals exhibit rapid decline, progressing much faster than other comparable patients.

The confluence of machine learning (ML) data-driven approaches and increased computational power, alongside access to the wealth of electronic health records (EHRs) and other emergent types of data (e.g., omics, imaging, mHealth), are accelerating the development of biomedical predictive models. Such models range from traditional statistical approaches (e.g., regression) through to more advanced deep learning techniques (e.g., convolutional neural networks, CNNs), and span different tasks (e.g., biomarker/pathway discovery, diagnostic, prognostic, etc.).

The Los Angeles (LA) PRISMS Center aims to be the leader in the development and application of mobile health (mHealth) technologies that deepen our scientific understanding and clinical management of diseases. Bringing together leading experts from UCLA and the University of Southern California (USC) in biomedical informatics, computer science, wireless health, environmental health science, and pediatrics, this Center supports innovative end-to-end software infrastructure for sensor-based health monitoring.

UCLA was home to the Centers Coordinating Center, promoting the common interests and ensuring the impact of innovations created by the various BD2K Centers of Excellence (COEs). The overarching goal was to help ensure that developed resources and tools were shared, integrated and mobilized; as well as effectively adopted by the broad scientific community.

Annually, it is estimated that more than 795,000 Americans experience a stroke. The severity of neurological damage due to an acute stroke is mitigated by the early restoration of blood flow to the affected area; and more people are now surviving strokes through earlier intervention with thrombolytic agents and interventional clot retrieval devices. Unfortunately, the rapid development of new drugs and devices in this area has made it difficult to provide treatment guidance for a given patient, and metrics for comparing outcomes between treatment groups are lacking.

Each year, almost half of all diagnosed primary brain tumors in the United States are Grade IV glioblastoma multiforme (GBMs). While recent efforts have begun to uncover the genetic pathways involved in this cancer’s etiology – and potential methods for treatment – arguably, no specific prognostic model has arisen (and been sufficiently validated) to provide widespread usability and individually tailored predictions about a patient’s prognosis, let alone suggest optimal treatment.

To meet ever-increasing computing needs and overcome power density limitations, the computing industry halted simple processor frequency scaling and entered the era of parallelization, with tens to hundreds of computing cores integrated in a single processor, and hundreds to thousands of computing servers connected in a warehouse-scale data center. However, such highly parallel, general-purpose computing systems still face serious challenges in terms of performance, power, heat dissipation, space, and cost.