The Pittsburgh Health Data Alliance (PHDA) https://healthdataalliance.com formed in 2015, is working closely with Amazon Web Services (AWS), https://aws.amazon.com through a machine learning research sponsorship. The goal is to advance innovation in cancer diagnostics, precision medicine, voice-enabled technologies, and medical imaging.
A Consortium was formed by University of Pittsburgh Medical College https://www.pitt.edu (UPMC), and Carnegie Mellon University (CMU) https://www.cmu.edu to study how “big data” used in healthcare could be used to transform the way diseases are treated and prevented. The data includes using patient information from EHRs, diagnostic imaging, prescriptions, genomic profiles, and insurance records.
Today, new machine learning technologies and advances in computing power as offered by Amazon SageMaker and Amazon EC2, are making it possible to rapidly translate insights discovered in the lab into treatments and services that could dramatically improve human health.
PHDA scientists from both Pitt and CMU expect to accelerate research and product commercialization across several projects. The projects include creating an individual risk score for every cancer patient to enable doctors to better predict the course of a person’s disease and response to treatment, use a patient’s verbal and visual cues to diagnose and treat mental health symptoms, and reduce medical diagnostic errors by mining all the data in a patient’s medical record.
One project will enable David Vorp, PhD, Associate Dean for Research at Pitt’s Swanson School of Engineering and the John A. Swanson Professor of Bioengineering along with his team use AWS resources to improve the diagnosis and treatment of abdominal aortic aneurysms. By using machine learning, the research team will work to develop an algorithm to provide clinicians with an objective predictive tool to guide surgical interventions before symptoms appear.
In addition, a CMU team led by Russel Schwartz, Ph.D., and Jian Ma, PhD will use AWS support to develop algorithms and software tools to better understand the origin and evolution of tumor cells. This project will use machine learning to gain insights into how tumors develop and be able to predict how they are likely to change and grow in the future.