Sign In

Communications of the ACM

ACM TechNews

­csf, Intel Join Forces to Develop Deep Learning Analytics For Healthcare


View as: Print Mobile App Share: Send by email Share on reddit Share on StumbleUpon Share on Hacker News Share on Tweeter Share on Facebook
Brain scans.

The University of California, San Franciscos Center for Digital Health Innovation is working with Intel to deploy and validate a deep learning analytics platform designed to improve care by helping clinicians make better treatment decisions, predict patient outcomes, and respond more nimbly in acute situations.

Credit: Medium

The University of California, San Francisco (UCSF) and Intel are using deep-learning techniques to power a scalable platform that will deliver clinical decision support and predictive analytics to healthcare professionals.

The platform will harness data collected for clinical care as well as big data from genomic sequencing, monitors, sensors, and wearables needed for acute clinical decision-making. A scalable "information commons" will integrate and store the massive amounts of data required for advanced data analytics and deep-learning algorithms. Intel technology will support the data management, curation, algorithm training, and testing processes. UCSF's deep-learning use cases will run in a distributed fashion on a central processing unit-based cluster.

The platform will be able to handle large datasets and scale for future use cases, including supporting convolutional neural network models, artificial networks patterned after living organisms, and large multidimensional datasets.

UCSF professor Michael Blum expects deep-learning capabilities will drive the transformation of healthcare. "Now that we have 'digitized' healthcare, we can begin utilizing the same technologies that have made the driverless car and virtual assistants possible and bring them to bear on vexing healthcare challenges such as predicting health risks, preventing hospital readmissions, analyzing complex medical images, and more," he says.

From UCSF News Center
View Full Article

 

Abstracts Copyright © 2017 Information Inc., Bethesda, Maryland, USA


 

No entries found