HPC Bests Physicians in Matching Heart Transplant Donors and Recipients

By Michael Feldman

September 13, 2011

Health care analytics is an emerging application area that promises to help cut costs and provide better patient outcomes. To reach that goal though requires sophisticated software that can mimic some of the intelligence of real live physicians. At Lund University and Skåne University in Sweden, researchers are attempting to do just that by building a model of heart-transplant recipients and donors to improve survival times.

The so-called “survival model” is designed to discover the optimal matches between recipients and donor for heart transplants. It takes into account such factors as age, blood type (both donor and recipient), weight, gender, age, and time during a transplant when there is no blood flow to the heart. Just analyzing those six variables leads to about 30,000 distinct combinations to track. When you want to match tens of thousands of recipients and donors across that spread of combinations, you need a rather sophisticated software model and some serious computing horsepower.

To build the application, the Lund researchers used MATLAB and a set of related MathWorks libraries, namely the Neural Network Toolbox, the Parallel Computing Toolbox, and the MATLAB Distributed Computing Server. With that, they built their predictive artificial neural network (ANN) models, in this case, a simulation that predicts survival rates for heart transplant patients based on the suitability of the donor match. The ANN models are “trained” using donor and recipient data encapsulated in two databases: the International Society for Heart and Lung Transplantation (ISHLT) registry and the Nordic Thoracic Transplantation Database (NTTD).

The key software technology for the ANN application is MathWorks’ Neural Network Toolbox.  The package contains tools for designing and simulating neural networks, which can be used for artificial intelligence-type applications such as pattern recognition, quantum chemistry, speech recognition, game-playing and process control.   These types of application don’t lend themselves easily to the type of formal analysis done in traditional computing.

For the ANN models, training involves correlating donor and recipient data, such that the risk factors are weighted accurately. If done correctly, the simulations can become adept at associating these factors with the heart transplant survival rates. In this case, the results from the simulations were used to pick out the best and worst donors for any particular recipient.

The ultimate goal is to determine the mean survival times after transplantation for waiting recipients, so that doctors can make the best possible decision with regard to matches. In the research study, they analyzed about 10,000 patients that had already received transplants in order to verify the accuracy of the algorithms.

What they found was that the ANN models could increase the five-year survival rate raised by 5 to 10 percent compared to the traditional selection criteria performed by practicing physicians. Perhaps more importantly, using a randomized trial based on preliminary results, approximately 20 percent more patients would be considered for transplantation under these models, says Dr. Johan Nilsson, Associate Professor in the Division of Cardiothoracic Surgery at Lund University.

Because of the combinatorial load of the recipient-donor variables, the models are very compute-intensive. On a relative small cluster, the MATLAB-derived ANN simulation took about five days. That was significantly better the open source software packages (R and Python) they started out with. Under that environment, runs took about three to four weeks and were beset with crashes and inaccurate results.

To run the simulation, the researchers used a nine-node Apple Xserve cluster (which includes a head node and a filesharing node), along with 16 TB of disk, all lashed to together with a vanilla GigE network. Memory size on the nodes ranged form 24 to 48 GB. According to Nilsson, with the latest MATLAB configuration, they use 64 CPUs to run the ANN simulation.

Nilsson, who is a physician, programmed the application himself, noting that the MATLAB environment was easy to set up and use, adding there was no need for deep knowledge of parallel computing. The biggest roadblock he encountered was the need to customize an error function (MATLAB Neural Network does not have any cross-entropy error routine.) There were also some problems encountered in setting up the Xserve cluster, but once they replaced Apple’s Xgrid protocol with the MATLAB Distributed Computing Server, many of those problems disappeared.

The Apple Xserve cluster is not exactly state of the art for high performance computing these day. Presumably with a late model HPC setup, they could cut the five-day turnaround time for the simulation even more, which would speed up the research even further.

In the short term, the Lund and Skåne team intend to continue to optimize the software and explore other solutions like regression tree and logistic regression algorithms, as well as add support for vector machines. In parallel, they want to start transitioning the technology into a clinical setting.

According to Nilsson, once they’ve fully cooked the models, they can do away with the high performance computing environment. “In a future clinical setting,” he says, “the application could be used on any desktop computer, and the matching process will take only seconds to a couple of minutes.”

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industy updates delivered to you every week!

AI-Focused ‘Genius’ Supercomputer Installed at KU Leuven

April 24, 2018

Hewlett Packard Enterprise has deployed a new approximately half-petaflops supercomputer, named Genius, at Flemish research university KU Leuven. The system is built to run artificial intelligence (AI) workloads and, as Read more…

By Tiffany Trader

New Exascale System for Earth Simulation Introduced

April 23, 2018

After four years of development, the Energy Exascale Earth System Model (E3SM) will be unveiled today and released to the broader scientific community this month. The E3SM project is supported by the Department of Energy Read more…

By Staff

RSC Reports 500Tflops, Hot Water Cooled System Deployed at JINR

April 18, 2018

RSC, developer of supercomputers and advanced HPC systems based in Russia, today reported deployment of “the world's first 100% ‘hot water’ liquid cooled supercomputer” at Joint Institute for Nuclear Research (JI Read more…

By Staff

HPE Extreme Performance Solutions

Hybrid HPC is Speeding Time to Insight and Revolutionizing Medicine

High performance computing (HPC) is a key driver of success in many verticals today, and health and life science industries are extensively leveraging these capabilities. Read more…

New Device Spots Quantum Particle ‘Fingerprint’

April 18, 2018

Majorana particles have been observed by university researchers employing a device consisting of layers of magnetic insulators on a superconducting material. The advance opens the door to controlling the elusive particle Read more…

By George Leopold

AI-Focused ‘Genius’ Supercomputer Installed at KU Leuven

April 24, 2018

Hewlett Packard Enterprise has deployed a new approximately half-petaflops supercomputer, named Genius, at Flemish research university KU Leuven. The system is Read more…

By Tiffany Trader

Cray Rolls Out AMD-Based CS500; More to Follow?

April 18, 2018

Cray was the latest OEM to bring AMD back into the fold with introduction today of a CS500 option based on AMD’s Epyc processor line. The move follows Cray’ Read more…

By John Russell

IBM: Software Ecosystem for OpenPOWER is Ready for Prime Time

April 16, 2018

With key pieces of the IBM/OpenPOWER versus Intel/x86 gambit settling into place – e.g., the arrival of Power9 chips and Power9-based systems, hyperscaler sup Read more…

By John Russell

US Plans $1.8 Billion Spend on DOE Exascale Supercomputing

April 11, 2018

On Monday, the United States Department of Energy announced its intention to procure up to three exascale supercomputers at a cost of up to $1.8 billion with th Read more…

By Tiffany Trader

Cloud-Readiness and Looking Beyond Application Scaling

April 11, 2018

There are two aspects to consider when determining if an application is suitable for running in the cloud. The first, which we will discuss here under the title Read more…

By Chris Downing

Transitioning from Big Data to Discovery: Data Management as a Keystone Analytics Strategy

April 9, 2018

The past 10-15 years has seen a stark rise in the density, size, and diversity of scientific data being generated in every scientific discipline in the world. Key among the sciences has been the explosion of laboratory technologies that generate large amounts of data in life-sciences and healthcare research. Large amounts of data are now being stored in very large storage name spaces, with little to no organization and a general unease about how to approach analyzing it. Read more…

By Ari Berman, BioTeam, Inc.

IBM Expands Quantum Computing Network

April 5, 2018

IBM is positioning itself as a first mover in establishing the era of commercial quantum computing. The company believes in order for quantum to work, taming qu Read more…

By Tiffany Trader

FY18 Budget & CORAL-2 – Exascale USA Continues to Move Ahead

April 2, 2018

It was not pretty. However, despite some twists and turns, the federal government’s Fiscal Year 2018 (FY18) budget is complete and ended with some very positi Read more…

By Alex R. Larzelere

Inventor Claims to Have Solved Floating Point Error Problem

January 17, 2018

"The decades-old floating point error problem has been solved," proclaims a press release from inventor Alan Jorgensen. The computer scientist has filed for and Read more…

By Tiffany Trader

Researchers Measure Impact of ‘Meltdown’ and ‘Spectre’ Patches on HPC Workloads

January 17, 2018

Computer scientists from the Center for Computational Research, State University of New York (SUNY), University at Buffalo have examined the effect of Meltdown Read more…

By Tiffany Trader

How the Cloud Is Falling Short for HPC

March 15, 2018

The last couple of years have seen cloud computing gradually build some legitimacy within the HPC world, but still the HPC industry lies far behind enterprise I Read more…

By Chris Downing

Russian Nuclear Engineers Caught Cryptomining on Lab Supercomputer

February 12, 2018

Nuclear scientists working at the All-Russian Research Institute of Experimental Physics (RFNC-VNIIEF) have been arrested for using lab supercomputing resources to mine crypto-currency, according to a report in Russia’s Interfax News Agency. Read more…

By Tiffany Trader

Chip Flaws ‘Meltdown’ and ‘Spectre’ Loom Large

January 4, 2018

The HPC and wider tech community have been abuzz this week over the discovery of critical design flaws that impact virtually all contemporary microprocessors. T Read more…

By Tiffany Trader

How Meltdown and Spectre Patches Will Affect HPC Workloads

January 10, 2018

There have been claims that the fixes for the Meltdown and Spectre security vulnerabilities, named the KPTI (aka KAISER) patches, are going to affect applicatio Read more…

By Rosemary Francis

Nvidia Responds to Google TPU Benchmarking

April 10, 2017

Nvidia highlights strengths of its newest GPU silicon in response to Google's report on the performance and energy advantages of its custom tensor processor. Read more…

By Tiffany Trader

Deep Learning at 15 PFlops Enables Training for Extreme Weather Identification at Scale

March 19, 2018

Petaflop per second deep learning training performance on the NERSC (National Energy Research Scientific Computing Center) Cori supercomputer has given climate Read more…

By Rob Farber

Leading Solution Providers

Lenovo Unveils Warm Water Cooled ThinkSystem SD650 in Rampup to LRZ Install

February 22, 2018

This week Lenovo took the wraps off the ThinkSystem SD650 high-density server with third-generation direct water cooling technology developed in tandem with par Read more…

By Tiffany Trader

Fast Forward: Five HPC Predictions for 2018

December 21, 2017

What’s on your list of high (and low) lights for 2017? Volta 100’s arrival on the heels of the P100? Appearance, albeit late in the year, of IBM’s Power9? Read more…

By John Russell

AI Cloud Competition Heats Up: Google’s TPUs, Amazon Building AI Chip

February 12, 2018

Competition in the white hot AI (and public cloud) market pits Google against Amazon this week, with Google offering AI hardware on its cloud platform intended Read more…

By Doug Black

HPC and AI – Two Communities Same Future

January 25, 2018

According to Al Gara (Intel Fellow, Data Center Group), high performance computing and artificial intelligence will increasingly intertwine as we transition to Read more…

By Rob Farber

US Plans $1.8 Billion Spend on DOE Exascale Supercomputing

April 11, 2018

On Monday, the United States Department of Energy announced its intention to procure up to three exascale supercomputers at a cost of up to $1.8 billion with th Read more…

By Tiffany Trader

New Blueprint for Converging HPC, Big Data

January 18, 2018

After five annual workshops on Big Data and Extreme-Scale Computing (BDEC), a group of international HPC heavyweights including Jack Dongarra (University of Te Read more…

By John Russell

Momentum Builds for US Exascale

January 9, 2018

2018 looks to be a great year for the U.S. exascale program. The last several months of 2017 revealed a number of important developments that help put the U.S. Read more…

By Alex R. Larzelere

Google Chases Quantum Supremacy with 72-Qubit Processor

March 7, 2018

Google pulled ahead of the pack this week in the race toward "quantum supremacy," with the introduction of a new 72-qubit quantum processor called Bristlecone. Read more…

By Tiffany Trader

  • arrow
  • Click Here for More Headlines
  • arrow
Share This