UCSF collaborates with Intel to deploy and validate deep learning analytics to improve healthcare

Jan 19 2017

UC San Francisco's Center for Digital Health Innovation (CDHI) today announced a collaboration with Intel Corporation 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.

The collaboration brings together Intel's leading edge computer science and deep learning capabilities with UCSF's clinical and research expertise to create a scalable, high-performance computational environment to support enhanced frontline clinical decision making for a wide variety of patient care scenarios. Until now, progress toward this goal has been difficult because complex, diverse datasets are managed in multiple, incompatible systems. This next-generation platform will allow UCSF to efficiently manage the huge volume and variety of data collected for clinical care as well as newer "big data" from genomic sequencing, monitors, sensors and wearables. These data will be integrated into a highly scalable "information commons" that will enable advanced analytics with machine learning and deep learning algorithms. The end result will be algorithms that can rapidly support data-driven clinical decision-making.

"While artificial intelligence and machine learning have been integrated into our everyday lives, our ability to use them in healthcare is a relatively new phenomenon," said Michael Blum, MD, associate vice chancellor for informatics, director of CDHI and professor of medicine at UCSF. "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. Deep learning environments are capable of rapidly analyzing and predicting patient trajectories utilizing vast amounts of multi-dimensional data. By integrating deep learning capabilities into the care delivered to critically injured patients, providers will have access to real-time decision support that will enable timely decision making in an environment where seconds are the difference between life and death. We expect these technologies, combined with the clinical and scientific knowledge of UCSF, to be made accessible through the cloud to drive the transformation of health and healthcare."

UCSF and Intel will work together to deploy the high-performance computing environment on industry standard Intel® Xeon® processor-based platforms that will support the data management and algorithm development lifecycle, including data curation and annotation, algorithm training, and testing against labeled datasets with particular pre-specified outcomes. The collaboration will also allow UCSF and Intel to better understand how deep learning analytics and machine-driven workflows can be employed to optimize the clinical environment and patient outcomes. This work will inform Intel's development and testing of new platform architectures for the healthcare industry.

"This collaboration between Intel and UCSF will accelerate the development of deep learning algorithms that have great potential to benefit patients," said Kay Eron, general manager of health and life sciences in Intel's Data Center Group. "Combining the medical science and computer science expertise across our organizations will enable us to more effectively tackle barriers in directing the latest technologies toward critical needs in healthcare."

The platform will enable UCSF's deep learning use cases to run in a distributed fashion on a central processing unit (CPU)-based cluster. The platform will be able to handle large data sets and scale easily for future use case requirements, including supporting larger convolutional neural network models, artificial networks patterned after living organisms, and very large multidimensional datasets. In the future, Intel expects to incorporate the deep learning analytics platform with other Intel analytics frameworks, healthcare data sources, and application program interfaces (APIs) - code that allows different programs to communicate - to create increasingly sophisticated use case algorithms that will continue to raise the bar in health and healthcare.

Source: University of California - San Francisco