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Big data is any size of data bigger than what
you're used to dealing with.
Once you're used to it, it's not big anymore.
I would tell you very simply, big data is
the digitization of life.
And from that, we try to garner meaning.
Big data is data that poses tremendous opportunities.
The data is extraordinarily complex, extraordinarily
diverse and typically extraordinarily large.
And this has challenged our ability to, uh,
deal with it, make inference from it.
I prefer the term data science, well, as opposed
to big data.
Data science is manipulating large amounts
of data, to answer questions that we couldn't
answer previously.
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Stanford Medicine is leading the biomedical
revolution in precision health, and biomedical
data science provides the foundation that
will allow us to realize this goal, and transform
biomedicine in the 21st century.
When we think about big data, we think about
all the excitement of all the different data
sources. But ultimately, it's really sort
of the discipline of data sciences that underlies
the methodology, the tools, the approaches,
the way we synthesize that information to
really extract knowledge from data.
Data science is what statistics became in
the 21st century, when other people besides
statisticians began taking it seriously as
a discipline.
But it's also has an emphasis on the fact
that we use data to generate our hypotheses
on how the world looks.
So, why should the average person care about
data science? Well, you know, it's interesting
to think about all the impacts that could
come.
We can learn so much about you, your DNA,
billions of other measurements that we can
make both on your molecules to understand
your health state, as well as all of the things
you do in terms of your activity. So, the
goal is really to make these measurements,
and understand people as soon as problems
arise, again, maybe before they even realize it.
As the father of a child that has benefited
directly from whole genome sequencing, I can
say unequivocally that a diagnosis has given
us much hope, and a lot to look forward to,
and a lot to work on.
Can we use information about a patient's genome,
or about their exposures out in the world
to make precise drug prescribing decisions,
and can we use information to figure out how
we can combine medications, perhaps even using
old drugs to treat new diseases?
We actually use large scale machine learning
to try to understand how to better design
drugs, and it's allowed us to make impacts
in many different areas, especially basic,
based upon physics and drug design in the
areas of Alzheimer's and infectious disease.
One of the things we've learned using big
data is that, how to make better surgical
decisions. And we've made discoveries about
which surgical procedures, for example, will
lead to greater mobility in people who have
cerebral palsy.
In our research, what we've done is taken
data from retinal scans which are acquired
at every visit here for patients with macular
degeneration, and created a mathematical model
in order to be able to predict which patients
might be getting worse, and which patients
should be safe from progression.
We're looking at how the big data about where
people come from, where they live, can help
us in terms of outreach, and help us in terms
of improving, um, the care that people with
Hepatitis B get.
Stanford has a lot of exciting advantages
in data science. We're all on the same campus,
people who do work in informatics and computer
science, people who understand the ethics
and the security issues related to big data.
We've been doing data science at Stanford
for many years. What's new these days is bringing
together the scientists that are generating
data with the ones that are, that understand
how to analyze the data, uh, to make a new
discoveries.
We've got people doing remarkably innovative
work across the spectrum, ranging from very
basic biology all the way out to the st-,
the studies of physical environment, social
environment, public health, um, and so forth.
Stanford School of Medicine has arguably the
best biomedical research program in the world,
right next to Stanford Hospital, one of the
world's leading hospitals. And the cool thing
is that all of our labs, and groups in computer
science, engineering, physics, biology, biochemistry,
we're all right next to each other.
This is really something that brings together
all of Stanford, and all of Stanford's strengths.
And then we can reach outside of the Stanford
campus to our neighbors who are in the technology
industry who've been using data science to
transform the world around us.
Oh, It's going to be very, very exciting,
because I think we are going to be making
more and more discoveries about how individuals
can prevent disease and increase their health,
and therefore their quality of life.
Looking into the future, I see us as a more
informed society. Uh, I see scientists, uh,
working in domains that they didn't expect
to work in because they have access to data
they didn't have access before.
And I do see that it's going to help the individual,
because I do believe this information is going
to translate into uh, decisions that will
empower the individual to make better choices.
I think that big data could serve as a democratization
of the scientific process, um, and also lead
to science moving towards a more patient-centered,
um, and patient-driven approach.
I think that the vast amount of data we're
going to generate on patients, whether it
be from blood samples, whether it be from
measuring them with new mobile sensors on
their wrists for measuring on the outside,
for measuring them in the hospital, that we're
going to have so much more data.
And I think we are going to be able to bring
it to bear, to diagnose the patients better
and then to target therapies better, so that
we can make a more personalized healthcare
system for the future.
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