Today, we're very excited to share with you

the Precision Medicine panel discussion.

I'll be moderating the session.

My name is Kay Erin.

I'm the general manager of Health and Life Sciences

at Intel.

And I'm excited to share with you

these three panelists that we have here.

First is John Madison.

He is a chief information medical officer

and he is part of Kaiser Permanente.

We're very excited to have you here.

Thank you, John.

>> Thank you.

>> We also have Naveen Rao.

He is the VP and general manager for the

Artificial Intelligence Solutions at Intel.

He's also the former CEO of Nervana,

which was acquired by Intel.

And we also have Bob Rogers, who's the chief data scientist

at our AI solutions group.

So, why don't we get started with our questions.

I'm going to ask each of the panelists to talk,

introduce themselves, as well as

talk about how they got started with AI.

So why don't we start with John?

>> Sure, so can you hear me okay in the back?

Can you hear?

Okay, cool.

So, I am a recovering evolutionary biologist

and a recovering physician

and a recovering geek.

And I implemented the health record system

for the first and largest region

of Kaiser Permanente.

And it's pretty obvious that most of the useful data

in a health record, in lies in free text.

So I started up a natural language processing team

to be able to mine free text about a dozen years ago.

So we can do things with that that you can't otherwise get

out of health information.

I'll give you an example.

I read an article online from

the New England Journal of Medicine

about four years ago that said

over half of all people who have had their spleen taken out

were not properly vaccinated for a common form of pneumonia,

and when your spleen's missing,

you must have that vaccine or you die a very sudden death

with sepsis.

In fact, our medical director in Northern California's

father died of that exact same scenario.

So, when I read the article,

I went to my structured data analytics team

and to my natural language processing team

and said please show me everybody who

has had their spleen taken out and hasn't been

appropriately vaccinated

and we ran through about 20 million records

in about three hours with the NLP team,

and it took about three weeks with a structured data

analytics team.

That sounds counterintuitive but

it actually happened that way.

And it's not a competition for time only.

It's a competition for quality

and sensitivity and specificity.

So we were able to indentify all of our members

who had their spleen taken out,

who should've had a pneumococcal vaccine.

We vaccinated them and there are a number of people

alive today who otherwise would've died

absent that capability.

So people don't really commonly associate

natural language processing with machine learning,

but in fact, natural language processing

relies heavily and is the first really,

highly successful example of machine learning.

So we've done dozens of similar projects,

mining free text data in millions of records

very efficiently, very effectively.

But it really helped advance the quality of care

and reduce the cost of care.

It's a natural step forward to go into the world of

personalized medicine with the arrival of

a 100-dollar genome, which is actually what it costs today

to do a full genome sequence.

Microbiomics, that is the ecosystem of bacteria

that are in every organ of the body actually.

And we know now that there is a profound influence

of what's in our gut and how we metabolize drugs,

what diseases we get.

You can tell in a five year old,

whether or not they were born by a vaginal delivery

or a C-section delivery

by virtue of the bacteria in the gut

five years later.

So if you look at the complexity of the data that exists

in the genome, in the microbiome,

in the health record with free text

and you look at all the other sources of data

like this streaming data from my wearable monitor

that I'm part of a research study

on Precision Medicine out of Stanford,

there is a vast amount of disparate data,

not to mention all the imaging,

that really can collectively produce

much more useful information to advance our

understanding of science, and to advance our understanding

of every individual.

And then we can do the mash up

of a much broader range of science in health care

with a much deeper sense of data from an individual

and to do that with structured

questions and structured data

is very yesterday.

The only way we're going to be able to disambiguate

those data and be able to operate on those data

in concert and generate real useful answers

from the broad array of data types

and the massive quantity of data,

is to let loose machine learning

on all of those data substrates.

So my team is moving down that pathway

and we're very excited about the future prospects

for doing that.

>> Yeah, great.

I think that's actually some of the things

I'm very excited about in the future

with some of the technologies we're developing.

My background, I started actually being fascinated with

computation in biological forms when I was nine.

Reading and watching sci-fi, I was kind of a big dork

which I pretty much still am.

I haven't really changed a whole lot.

Just basically seeing that machines really aren't

all that different from biological entities, right?

We are biological machines and kind of

understanding how a computer works

and how we engineer those things and

trying to pull together concepts that learn from biology

into that has always been a fascination of mine.

As an undergrad, I was in the EE, CS world.

Even then, I did some research projects around that.

I worked in the industry for about 10 years

designing chips, microprocessors,

various kinds of ASICs,

and then actually went back to school,

quit my job, got a Ph.D. in neuroscience,

computational neuroscience,

to specifically understand what's the state of the art.

What do we really understand about the brain?

And are there concepts that we can take and bring back?

Inspiration's always been we want to...

We watch birds fly around.

We want to figure out how to make something that flies.

We extract those principles, and then build a plane.

Don't necessarily want to build a bird.

And so Nervana's really was

the combination of all those experiences,

bringing it together.

Trying to push computation in a new a direction.

Now, as part of Intel, we can really add

a lot of fuel to that fire.

I'm super excited to be part of Intel

in that the technologies that we were developing

can really proliferate and be applied to health care,

can be applied to Internet, can be applied

to every facet of our lives.

And some of the examples that John mentioned

are extremely exciting right now

and these are things we can do today.

And the generality of these solutions