How AI and Deep Learning Could Remake Global Healthcare

Christopher Bouton, PhD, a self-described molecular neurobiologist turned entrepreneur, had such a positive experience starting and running a company that he decided to do it twice.

After a five-year stint at pharmaceutical/biotech giant, Pfizer, the Johns Hopkins grad started a company called Entagen, which developed semantic-based analytics for the healthcare sector. After five years running Entagen, Thomson Reuters acquired the company in what Dr. Bouton called a “successful exit.”

Dr. Bouton spent a few years under the Thomson Reuters umbrella, but the entrepreneurial itch soon returned. “[In] 2016 while I was sort of contemplating what to do next, I started to take note of all of these deep learning approaches that were starting to be talked about. What everyone’s talking about is [artificial intelligence]. The reason that they’re talking about AI is because of these deep learning algorithms. And so I got interested in what they were and how they worked.”

That’s how Vyasa Analytics, Bouton’s latest venture, came to be in early 2017. The company provides deep learning software and analytics for the healthcare and life sciences industry. Bouton spoke to Chief Executive about Vyasa, how running a tech company means catering to people’s creative side, the potential of AI and big data in the healthcare sector, and more.

Below are excerpts from the interview.

The name Vyasa and the story behind that name is kind of interesting. How did you come up with that name?

I spent four years in India as a boy and I love India. I actually remember that time in my life better than I do [the] three or so years afterwards. It’s just such a vibrant, incredible place, and Vyasa is the name of ancient Hindu sage. He was referred to as the compiler of knowledge and he wrote the “Mahabharata,” which is a very famous text. He compiled many of the Vedas, which are the basis of much of the Hindu knowledge base. And so I love this idea of a compiler who pulls together knowledge. I saw these AI approaches as sort of similar in capability, and thus got excited about the name.

What did you learn from being the CEO of Entagen and how have you applied those lessons to your current role at Vyasa?

That’s a great question. One of the key learnings and one of the things that I love most about running a company is building a team and [learning] how to foster that team, how to grow that team, how to give that team the tools and resources that they need to operate effectively. And so, I really see my job as supporting the members of the company and learning how to do that effectively. [I’m] learning that software development or technology development on the cutting edge is really, in part an art form. And these are very creative acts that the people on these teams are performing.

And so, you have to foster creativity and arts as part of your job with running a team like this. I brought that to Vyasa as well, we’re attempting to address really big challenging problems, but trying to do so for the sake of really advancing technologies and approaches that are important for humanity. And so, it’s a place that’s really exciting to be.

What kind of potential do you see from the healthcare perspective for AI and big data?

Yeah, that’s a great question. So, I see big data and the big data phase as the beginning of our interest and ability to just grapple with the amount of digital information we’re all creating now. There’s this sort of digital tsunami, let’s say, that we’ve all created and the big data phase was when we needed to just figure out how to handle it, how to store it, how to run analytics against it. You know, all the plumbing, all the infrastructure necessary to just handle lots of data.

But just having a lot of data really isn’t enough. You need to know how to effectively gather insights from all of that information and that’s where I see AI really helping us do a better job than what’s been possible before.

You have to be able to operate across the different kinds of data. For example, right now, many people refer to unused data as dark data and all of it is also not very well-connected and people referred to that as data silos. And I see a role for AI in being able to effectively harness many different data silos and climb in to what is otherwise dark data and extract more meaning from it…it’s really that extraction and surfacing of insights from lots of data that helps humans make better decisions which is, really, at the end of the day the name of the game enable more effective decision-making at the moment in which you need to make a call.

What kinds of markets are you guys serving in? Is it mostly to the provider side? Is it mostly to the payer side or is it any kind of entity that needs that data? I mean, that’s such a wide open market…so I’m curious to see where you’re focusing your efforts.

In the healthcare space, the payers and the providers…are those that are the most interested in extracting meeting and then conducting analysis on large scale data. And this also extends into the life sciences, the R&D, and the pharma space…there’s the continuum back all the way to how drugs are developed, how therapies are developed and brought to market. So that’s another area we are focusing. Along the way, we’re also focusing on some key types of analytics that can be conducted with deep learning.

“[I’m] learning that software development or technology development on the cutting edge is it’s really in part an art form. And these are very creative acts that the people on these teams are performing.”

We really wanted to sort of separate the hype from the reality of what the value of these technologies is in these spaces and so we focused in four key areas across articles. Those key areas are text analytics, image analytics, compound or small compound analytics, and then predictive analytics or quantitative analytics.

And so, in those areas we’ve seen real game- changing value of deep learning approaches for those data types. You know what that means is that there’s really in many different verticals that use, for example, text or imagery. And so, we’ve seen that even though we started in the life sciences and healthcare space, we’ve started to see interest already in a wide range of other verticals— legal, finance, marketing.

Where do you see Vyasa in the next year or two?

We spend every day just trying to build the best solutions that we can and support server clients as best as we can. And, you know, we intend to very much stay a solutions-oriented company, building software and capabilities that help to address the kinds of use cases that we’re seeing in the market. We are excited about the growth opportunity that we see. You know, within the life science or in healthcare space, but [in some cases] more broadly across verticals. And so I’d love to see the company grow and started to be able to address the number of these use cases across verticals. But at the same time, you know, I say this often, it’s really just doing the best job you can every single day and if you do that right, then the future is bright, and you sort of let the market tell you where to go.

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