AWS Partnership Advances Use of Machine Learning in Clinical Care –

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  • October 9, 2020
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By Samantha McGrail

October 09, 2020 – Two projects sponsored by Amazon Web Services (AWS) and the Pittsburgh Health Data Alliance (PHDA) have generated solid use cases for machine learning in clinical care.

Amazon Web Services (AWS) and the Pittsburgh Health Data Alliance (PHDA) collaborated in August 2019 to advance innovation in areas including cancer diagnostics, precision medicine, electronic health records, and medical imaging. 

Through the collaboration, researchers from the University of Pittsburgh Medical Center (UPMC), University of Pittsburgh, and Carnegie Mellon University (CMU) received support from Amazon Search Awards on top of existing support from PHDA to use machine learning to dive into various projects.

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One of those projects examined machine learning techniques to help experts study breast cancer risk and understand what drives tumor growth. 

Led by Shandong Wu, an associate professor at the University of Pittsburgh department of radiology, a research team analyzed 452 normal mammograms from 226 patients in order to predict the short-term risk of developing breast cancer. 

Wu and his team, who included experts in computer vision, deep learning, bioinformatics, and breast cancer imaging, used two machine learning models and found that both models consistently outperformed in the area of breast density.

Specifically, the team’s model demonstrated between 33 percent and 35 percent improvement over the existing models, researchers highlighted. 

“This preliminary work demonstrates the feasibility and promise of applying deep-learning methodologies for in-depth interpretation of mammogram images to enhance breast cancer risk assessment,” Wu said in the announcement. 

“Identifying additional risk factors for breast cancer, including those that can lead to a more personalized approach to screening, may help patients and providers take more appropriate preventive measures to reduce the likelihood of developing the disease or catching it early on when interventions are most effective.” 

Another project led by Eva Szigethy, clinical researcher at UPMC and Louis-Phillippe Morency, associate professor of computer science at CMU, used machine learning to measure changes in an individual’s behavior to diagnosis depression.

Their machine learning models are trained on tens of thousands of language, acoustic, and visual modalities to identify biomarkers for depression. The biomarkers will be compared to results from traditional clinical assessments to determine the accuracy of the machine learning models with identifying depression.

“New insights to increase the accuracy, efficiency, and adoption of depression screening have the potential to impact millions of patients, their families, and the healthcare system as a whole,” Morency stated. 

AWS and PHDA noted that the projects on breast cancer and depression are just the start when it comes to research collaboration to improve patient care. 

Teams of researchers, healthcare professionals, and machine learning experts will continue to work to understand the risk of aneurysms, predict how cancer cells progress, and aim to improve the electronic health records system. 

“Amazon is excited and encouraged by the progress these researchers are making and how machine learning is central to their work,” said An Luo, senior technical program manager for academic programs at Amazon AI. 

“We look forward to continuing to share how this unique collaboration between the PHDA and AWS is enabling new discoveries to help patients on a global scale.”

For example, David Vorp, PhD, associate dean for research at UPMA, and his research team employed AWS cloud resources to boost the diagnosis and therapy of abdominal aortic aneurysms.

And a CMU research team led by Russell Schwartz, PhD, and Jian Ma, PhD, used machine learning to develop algorithms and software tools to better understand cell origin and evolution. 

“With the latest advances in machine learning, we are developing an algorithm that will provide clinicians with an objective, predictive tool to guide surgical interventions before symptoms appear, improving patient outcomes,” Vorp said in the August announcement.