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How GPUs Are Helping Paris' Public Hospital System Combat the Spread of COVID-19

16/10/2020

In the battle against COVID-19, Greater Paris University Hospitals Public Assistance Hospital of Paris (AP-HP is the French acronym) isn't just on the medical front lines - it's on the data front lines as well.

With a network of 39 hospitals treating 8.3 million patients each year, AP-HP is a major actor in the fight against COVID-19.

Along with its COVID-19 cases comes an awful lot of data, including now geodata that can potentially help lessen the impact of the pandemic. AP-HP, which partners with seven universities, already had the ability to analyze large amounts of medical data. It had previously created dashboards that combined cancer cases and geodata. So, it was logical to pursue and extend its role during the pandemic.

The expected volume of COVID-19 data and geodata would probably have tested AP-HP's data crunching capacity. To mitigate this critical challenge, the hospital's information systems administrators turned to Kinetica, a provider of streaming data warehouses and real-time analytics and a member of the NVIDIA Inception program for AI startups.

Kinetica's offering harnesses the power of NVIDIA GPUs to quickly convert case location data into usable intelligence. And in the fight against COVID-19, speed is everything.

The project team also used NVIDIA RAPIDS to speed up the machine learning algorithms integrated into the platform. RAPIDS accelerates analytics and data science pipelines on NVIDIA GPUs by taking advantage of GPU parallelism and high memory bandwidth.

Having the ability to perform this type of analysis in real time is really important during a pandemic, said Hector Countouris, the project lead at AP-HP. And more data is coming.

Analyzing COVID Contact Data What Countouris and his colleagues are most focused on is using COVID-related geodata to understand where virus hot spots are and the dynamic of the outbreak. Looking for cluster locations can help decision-making at the district or region level.

In addition, they're looking at new signals to improve early detection of COVID patients. This includes working with data from other regional agencies.

If patients are diagnosed with COVID, they'll be asked by the relevant agencies via a phone call about their recent whereabouts and contacts to help with contact tracing. This is the first time that a wide range of data from different partners in the Paris area will be integrated to allow for contact tracing and timely alerts about a potential exposure. The result will be a newfound ability to see how clusters of COVID-19 cases evolve.

We hope that in the near future we will be able to follow how a cluster evolves in real time, said Countouris.

The goal is to enable public health decision-makers to implement prevention and control measures and assess their effectiveness. The data can also be integrated with other demographic data to study the viral spread and its possible dependency on socio-economics and other factors.

Attacking Bottlenecks with GPUs Prior to engaging with Kinetica, such data-intensive projects involved so much time for loading the data that they couldn't be analyzed quickly enough to deliver real-time benefits.

Now, I don't feel like I have a bottleneck, said Countouris. We are continuously integrating data and delivering dashboards to decision makers within hours. And with robust real-time pipelines allowing for continuous data ingestion, we can now focus on building better dashboards.

In the past, to get data in a specific and usable format, they would need to do a lot of pre-processing. With Kinetica's Streaming Data Warehouse powered by NVIDIA V100 Tensor Core GPUs, that's no longer the case. Users can access the much richer datasets they demand.

Kinetica's platform is available on NVIDIA NGC, a catalog of GPU-optimized AI containers that let enterprises quickly operationalize extreme analytics, machine learning and data visualization. This eliminates complexity and lets organizations deploy cloud, on-premises or hybrid models for optimal business operations.

I don't think we could meet user expectations for geodata without GPU power, he said. There is just too much data and geodata to provide for too many users at the same time.

AP-HP's COVID-related work has already built a foundation upon which to do follow-up work related to emergency responses in general. The hospital information system's interest for that kind of data is far from over.

The fact that we helped the decision-making process and that officials are using our data is the measure of success, said Countouris. We have a lot to do. This is only the beginning.

Countouris presented the team's work last week at the GPU Technology Conference. Registered GTC attendees can view the talk on demand. It will be available for replay to the general public early next month.

Kinetica will also be part of the NVIDIA Startup Village Booth at the HLTH conference, presenting on Oct. 16 at 2 p.m. Pacific time.
LINK: https://blogs.nvidia.com/blog/2020/10/15/paris-university-hospital-kin...
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