NVIDIA Article Details How Deep 6’s NLP Speeds the Search for Clinical Study Recruits

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The team at NVIDIA published an article discussing how Deep 6 is leveraging NVIDIA hardware to train and improve the NLP and AI that power our patient-matching platform.

 

Here’s a snippet from the article:

 

“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.

The article goes on to detail how Deep 6 AI is developing our own NLP to tackle the unique challenges of unstructured medical records and how our unique approach to developing our platform is what allows us to return results in seconds from millions of records, where other systems take hours or even weeks.

 

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