From ER Visit to AI Startup, CloudMedx Pursues Predictive Healthcare Models

Twice Sahar Arshad’s father-in-law went to an emergency room in Pakistan complaining of frequent headaches. Twice doctors sent him home with a diagnosis of allergies.

Turns out he was suffering from a subdural hematoma — bleeding inside the head. Following the second misdiagnosis, he went into a coma and required emergency brain surgery (and made a full recovery.)

Arshad and her husband, Tashfeen Suleman — both computer scientists living in Bellevue, Wash., at the time — afterwards tried to get to the root of the inaccurate diagnoses. The hematoma turned out to be a side effect of a new medication Suleman’s father had been prescribed a couple weeks prior. And he lacked physical symptoms like slurred speech and difficulty walking, which would have prompted doctors to order a CT scan and detect the bleeding earlier.

Too Much Data, Too Little Time

It’s a common problem, Arshad and Suleman found. Physicians often have to rely on limited information, either because there’s insufficient data on a patient or because there’s not enough time to analyze large datasets.

The couple thought AI could help address this challenge. In late 2014, they together founded CloudMedx, a Palo Alto-based startup that develops predictive healthcare models for health providers, insurers and patients.

A member of the NVIDIA Inception virtual accelerator program, CloudMedx is working with the University of California, San Francisco; Barrow Neurological Institute, a member of Dignity Health, a nonprofit healthcare organization; and some of the largest health insurers in the country.

Its AI models, trained using NVIDIA V100 Tensor Core GPUs through Amazon Web Services, can help automate medical coding, predict disease progression and determine the likelihood a patient may have a complication and need to be readmitted to the hospital within 30 days.

“What we’ve built is a natural language model that understands how different diseases, symptoms and medications are related to each other,” said Arshad, chief operating officer at CloudMedx. “If we’d had this tool in Tashfeen’s father’s case, it would have flagged the risk of internal head hemorrhaging and recommended obtaining a CT scan.”

Working with an AI to Risk Assessment

The CloudMedx team has developed a deep neural network that can process medical data to provide risk assessment scores, saving clinicians time and providing personalized insight for patients. It’s trained on a dataset of 54 million patient encounters.

In a study to evaluate its deep learning model, the clinical AI tool took a mock medical exam — and outperformed human doctors by 10 percent, on average. On their own, physicians scored between 68 to 81 percent. When taking the exam along with CloudMedx AI, they achieved a high score of 91 percent.

The startup’s AI models are used in multiple tools, including a coding analyzer that converts doctor’s notes into a series of medical codes that inform the billing process, as well as a clinical analyzer that evaluates a patient’s health records to generate risk assessments.

CloudMedx is collaborating with UCSF’s Division of Gastroenterology to stratify patients awaiting liver transplants based on risk, so that patients can be matched with donors before the tumor progresses too far for a transplant.

The company is also working with one of the largest health insurers in the U.S. to better identify congestive heart failure patients with a high risk of readmission to the hospital. With these insights, health providers can follow up more often with at-risk patients, reducing readmissions and potentially saving billions of dollars in treatment costs.

Predictive Analytics for Every Healthcare Player

Predictive analytics can even improve the operational side of healthcare, giving hospitals a heads-up when they might need additional beds or staff members to meet rising patient demand.

“It’s an expensive manual process to find additional resources and bring on extra nurses at the last minute,” Arshad said. “If hospitals are able to use AI tools for surge prediction, they can better plan resources ahead of time.”

In addition to providing new insights for health providers and payers, these tools save time by processing large amounts of medical data in a fraction of the time it would take humans.

CloudMedx has also developed an AI tool for patients. Available on the Medicare website to its 53 million patient beneficiaries, the system helps users access their own claims data, correlates a person’s medical history with symptoms, and will soon also estimate treatment costs.

NVIDIA Inception Program

As members of the NVIDIA Inception program, the CloudMedx team was able to reach out to NVIDIA developers and the company’s healthcare team for help with some of the challenges they faced when scaling up for cloud deployment.

Inception helps startups during critical stages of product development, prototyping and deployment with tools and expertise to help early-stage companies grow.

Both Suleman and Arshad have spoken at NVIDIA’s annual GPU Technology Conference, with Arshad participating in a Women@GTC healthcare panel last year. The conference has helped the team meet some of their customers, said Arshad, who’s also a finalist for Entrepreneur of the Year at the 2020 Women in IT Awards New York.

Check out the healthcare track for GTC, taking place in San Jose, March 22-26.

The post From ER Visit to AI Startup, CloudMedx Pursues Predictive Healthcare Models appeared first on The Official NVIDIA Blog.

From ER Visit to AI Startup, CloudMedx Pursues Predictive Healthcare Models

Twice Sahar Arshad’s father-in-law went to an emergency room in Pakistan complaining of frequent headaches. Twice doctors sent him home with a diagnosis of allergies. Turns out he was suffering from a subdural hematoma — bleeding inside the head. Following the second misdiagnosis, he went into a coma and required emergency brain surgery (and made Read article >

The post From ER Visit to AI Startup, CloudMedx Pursues Predictive Healthcare Models appeared first on The Official NVIDIA Blog.

A New Frontier for PC Gaming: How GeForce NOW’s Game Library Continues to Evolve

Giving what we love about PC gaming to more people. That’s the vision that inspired our journey to develop GeForce NOW into an open platform that welcomed gamers, developers and publishers.

For gamers, we provide the power of a GeForce GPU to play the games you love from the cloud.

For developers, we used our beta phase to show how GeForce NOW could expand a game’s audience to low-end PC laptops, Mac computers, Android devices and, soon, to Chromebooks, without any porting effort.

For publishers, we connected gamers directly to game stores, so they maintain control of their content and we stay out of their economics.

Many in the games industry are riding the wave of excitement GeForce NOW has generated. And we’re just getting started.

A New Frontier

Earlier this month, we passed a milestone on our cloud gaming journey by removing the waitlist and opening our doors to more gamers. Over 1 million new gamers have taken to the cloud by signing up for a free plan or upgrading to the Founders membership, which includes a 90-day free trial.

This trial is an important transitional period where gamers, developers and publishers can try the premium experience with minimal commitment while we continue to refine our offering.

As we approach a paid service, some publishers may choose to remove games before the trial period ends. Ultimately, they maintain control over their content and decide whether the game you purchase includes streaming on GeForce NOW. Meanwhile, others will bring games back as they continue to realize GeForce NOW’s value (stay tuned for more on that).

As the transition period comes to completion, game removals should be few and far between, with new games added to GeForce NOW each week.

A Fresh Horizon

Countless developers and publishers are embracing the opportunity to expand the number of gamers who can play their games on GeForce NOW.

Look no further than the recent news that the most anticipated game of 2020, CD PROJEKT RED’s Cyberpunk 2077, will be available on GeForce NOW the day it’s released. That adds to the hundreds of great games currently instantly available, among them: 30 of the biggest, most played free-to-play games — including Fortnite from Epic Games, League of Legends from Riot, Warframe from Digital Extremes and World of Tanks from Wargaming.

And we have an additional 1,500 games in our onboarding queue, from publishers that share a vision of expanding PC gaming to more people.

The world’s largest gaming platform — PC gaming — continues to evolve. We’re looking forward to giving this power to even more players, and ushering in a new generation of PC gamers.

As gamers ourselves, nothing sounds better.

The post A New Frontier for PC Gaming: How GeForce NOW’s Game Library Continues to Evolve appeared first on The Official NVIDIA Blog.

A New Frontier for PC Gaming: How GeForce NOW’s Game Library Continues to Evolve

Giving what we love about PC gaming to more people. That’s the vision that inspired our journey to develop GeForce NOW into an open platform that welcomed gamers, developers and publishers. For gamers, we provide the power of a GeForce GPU to play the games you love from the cloud. For developers, we used our Read article >

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Putting AI on Trials: Deep 6 Speeds Search for Clinical-Study Recruits

Bringing a new medical treatment to market is a slow, laborious process — and for a good reason: patient safety is the top priority.

But when recruiting patients to test promising treatments in clinical trials, the faster the better.

“Many people in medicine have ideas of how to improve healthcare,” said Wout Brusselaers, CEO of Pasadena, Calif.-based startup Deep 6 AI. “What’s stopping them is being able to demonstrate that their new process or new drug works, and is safe and effective on real patients. For that, they need the clinical trial process.”

Over the past decade, the number of cancer clinical trials has grown 17 percent a year, on average. But nearly a fifth of these studies fail to recruit a sufficient number of participants that fit sometimes very specific trial criteria after three years of searching — and the problem isn’t getting any simpler.

“In the age of precision medicine, clinical trial criteria are getting more challenging,” Brusselaers said. “When developing a drug that is targeting patients with a rare genetic mutation, you have to be able to find those specific patients.”

By analyzing medical records with AI, Deep 6 can identify a patient population for clinical trials within minutes, accelerating what’s traditionally a months-long process. Major cancer centers and pharmaceutical companies, including Cedars Sinai Medical Center and Texas Medical Center, are using the AI tool. They’ve matched more than 100,000 patients to clinical trials so far.

The startup’s clinical trial acceleration software has specific tools to help hospitals recommend available trials to patients and to help pharmaceutical companies track and accelerate patient recruitment for their studies. Future versions of the software could also be made available for patients to browse trials.

A Match Made in AI

Deep 6 AI is a member of the NVIDIA Inception virtual accelerator program, which helps startups scale faster. The company uses an NVIDIA TITAN GPU to accelerate the development of its custom AI models that analyze patient data to identify and label clinical criteria relevant to trials.

“It’s more efficient and less expensive for us to develop our models on premises,” Brusselaers said. “We could turn around models right away and iterate faster, without having to wait to rerun the code.”

While the tool can be used for any diagnostic area or medical condition, Brusselaers says over a quarter of trials on the platform are oncology studies, followed closely by cardiology.

Trained on a combination of open-source databases and real-world data from Deep 6’s partners, the AI models first identify specific mentions of clinical terminology and medical codes in patient records with natural language processing.

Additional neural networks analyze unstructured data like doctor’s notes and pathology reports to gather additional information about a patient’s symptoms, diagnoses and treatments — even detecting potential conditions not mentioned in the medical records.

Deep 6’s tool then creates a patient graph that represents the individual’s clinical profile. These graphs can easily be matched by doctors and researchers to develop trial cohorts, upgrading a time-consuming, often unfruitful manual process.

Researchers at Los Angeles’ Cedars-Sinai Smidt Heart Institute — one of the startup’s clients — had enrolled just two participants for a new clinical trial after six months of recruitment effort. Using Deep 6 AI software, they found 16 qualified candidates in an hour.

Texas Medical Center, a collection of over 60 health institutions, is rolling out Deep 6 software across its network to replace the typical process of finding clinical trial candidates, which requires associates to manually flip through thick folders of medical records.

“It’s just a long slog to find patients for clinical trials,” said Bill McKeon, CEO of Texas Medical Center. Using Deep 6’s software tool “is just completely transforming.”

McKeon says in one case, it took six months to find a dozen eligible patients for a trial with traditional recruitment efforts. The same matching process through Deep 6’s software found 80 potential participants in minutes.

The post Putting AI on Trials: Deep 6 Speeds Search for Clinical-Study Recruits appeared first on The Official NVIDIA Blog.

Putting AI on Trials: Deep 6 Speeds Search for Clinical-Study Recruits

Bringing a new medical treatment to market is a slow, laborious process — and for a good reason: patient safety is the top priority. But when recruiting patients to test promising treatments in clinical trials, the faster the better. “Many people in medicine have ideas of how to improve healthcare,” said Wout Brusselaers, CEO of Read article >

The post Putting AI on Trials: Deep 6 Speeds Search for Clinical-Study Recruits appeared first on The Official NVIDIA Blog.

Hail Yeah! How Robotaxis Will Change the Way We Move

From pedicabs to yellow cabs, hailing rides has been a decades-long convenience. App-based services like Uber and Lyft have made on-demand travel even faster and easier.

With the advent of autonomous vehicles, ride-hailing promises to raise the bar on safety and efficiency. Known as robotaxis, these shared vehicles are purpose-built for transporting groups of people along optimized routes, without a human driver at the wheel.

The potential for a shared autonomous mobility industry is enormous. Financial services company UBS estimates that robotaxis could create a $2 trillion market globally over the next decade, with each vehicle generating as much as $27,000 annually.

In dense urban environments, like New York City, experts project that a taxi fleet converted to entirely autonomous vehicles could cut down commutes in some areas from 40 minutes to 15.

On top of the economic and efficiency benefits, autonomous vehicles are never distracted or drowsy. And they can run 24 hours a day, seven days a week, expanding mobility access to more communities.

To transform everyday transportation, the NVIDIA DRIVE ecosystem is gearing up with a new wave of electric, autonomous vehicles.

Up for Cabs

Building a new kind of vehicle from square one requires a fresh perspective. That’s why a crop of startups and technology companies have begun to invest in the idea of a shared car without a steering wheel or pedals.

Already transporting riders in Florida and San Jose, Calif. retirement communities, Voyage is deploying low-speed autonomous vehicles with the goal of widely expanding safe mobility. The company is using DRIVE AGX to operate its SafeStop supercharged automatic braking system in its current fleet of vehicles.

Optimus Ride is a Boston-based self-driving technology company developing systems for geo-fenced environments — pre-defined areas of operation, like a city center or shipping yard.

Its electric, autonomous vehicles run on the high-performance, energy-efficient NVIDIA DRIVE platform, and were the first such vehicles to run in NYC as part of a pilot launched in August.

Optimus Ride

Leveraging the performance of NVIDIA DRIVE AGX Pegasus, which can achieve up to 320 trillion operations per second, smart mobility startup WeRide is developing level 4 autonomous vehicles to provide accessible transportation to a wide range of passengers.

Starting from scratch, self-driving startup and DRIVE ecosystem member Zoox is developing a purpose-built vehicle for on-demand, autonomous transportation. Its robotaxi encompasses a futuristic vision of everyday mobility, able to drive in both directions.

Zoox says it plans to launch its zero-emissions vehicle for testing this year, followed by an autonomous taxi service.

At GTC China in December, ride-hailing giant Didi Chuxing announced it was developing level 4 autonomous vehicles for its mobility services using NVIDIA DRIVE and AI technology. Delivering 10 billion passenger trips per year, DiDi is working toward the safe, large-scale application of autonomous driving technology.

DiDi

Sharing Expertise for Shared Mobility

When it comes to industry-changing innovations, sometimes two (or three) heads are better than one.

Global automakers, suppliers and startups are also working to solve the question of shared autonomous mobility, collaborating on their own visions of the robotaxi of the future.

In December, Mercedes-Benz parent company Daimler and global supplier Bosch launched the first phase of their autonomous ride-hailing pilot in San Jose. The app-based service shuttles customers in an automated Mercedes-Benz S-Class monitored by a safety driver.

The companies are collaborating with NVIDIA to eventually launch a robotaxi powered by NVIDIA DRIVE AGX Pegasus.

Daimler

Across the pond, autonomous vehicle solution provider AutoX and Swedish electric vehicle manufacturer NEVS are working to deploy robotaxis in Europe by the end of this year.

The companies, which came together through the NVIDIA DRIVE ecosystem, are developing an electric autonomous vehicle based on NEVS’ mobility-focused concept and powered by NVIDIA DRIVE. The goal of this collaboration is to bring these safe and efficient technologies to everyday transportation around the world.

Startup Pony.AI is also collaborating with global automakers such as Toyota and Hyundai, developing a robotaxi fleet with the NVIDIA DRIVE AGX platform at its core.

As the NVIDIA DRIVE ecosystem pushes into the next decade of autonomous transportation, safer, more convenient rides will soon just be a push of a button away. At GTC 2020, attendees will get a glimpse to just where this future is going — register today with code CMAUTO for a 20 percent discount.

The post Hail Yeah! How Robotaxis Will Change the Way We Move appeared first on The Official NVIDIA Blog.

Hail Yeah! How Robotaxis Will Change the Way We Move

From pedicabs to yellow cabs, hailing rides has been a decades-long convenience. App-based services like Uber and Lyft have made on-demand travel even faster and easier. With the advent of autonomous vehicles, ride-hailing promises to raise the bar on safety and efficiency. Known as robotaxis, these shared vehicles are purpose-built for transporting groups of people Read article >

The post Hail Yeah! How Robotaxis Will Change the Way We Move appeared first on The Official NVIDIA Blog.