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CHRIS JOSEF: Sepsis is the body trying to fight an infection.
That infection has got out of control,
and you're in a state of attrition warfare.
SHAMIN NEMATI: What really attracted me to sepsis research
is if you predict it early and put patients on antibiotics,
you can potentially save lives.
There has been a few attempts in the past 10, 15 years
to predict sepsis.
However, many of those algorithms,
they had a fairly low performance.
Over the past few years, with better analytic tools
such as deep learning, artificial intelligence tools,
we have been able to improve the accuracy of these models.
Definitely, setting up the data was probably 70% of the work.
It took us a year to figure out how
to get the data in what format.
There were times that I thought, it's
just not going to be possible.
The first time we built the model,
it was based on only 400 patients.
And our latest data pool is about 450,000 patients.
With the advent of better AI algorithms,
you're able to look at, now, many,
many different indicators.
In our latest work, we were looking
at 65 different indicators.
Some of them comes from laboratory,
from patient's vital signs.
Some of them are coming from bedside monitor data.
And it's becoming impossible to process this data
on our desktops locally.
So that's where some of the cloud resources
becomes very, very useful.
ASHISH SHARMA: Shamin is one of the smartest people you'll
find anywhere in the area of time series
analysis and deep learning.
He's working much more on the AI side of things.
I'm working much more on the systems side of things.
In our current system, we've tightly
coupled how we're fetching data from bedside data sets.
On the Google Cloud, we have the data layer,
where all the layer's coming in, and it's
being refined and getting to a point
where it can then go down the processing layer.
At that stage, we found Dataflow ends up
being a very effective tool.
The nice managed service on the Google Cloud platform
that allows me to transform data and has a very nice user
interface to try an auto those jobs, and reuse those jobs.
Once data has been cleansed, gotten ready for computing,
we make use of TensorFlow.
TensorFlow is this core library that we
use for developing and then deploying
[INAUDIBLE] algorithms.
The way the overall system was developed, one of our goals
was to make sure that one can easily add and develop
new algorithms, such as a sepsis prediction algorithm.
SHAMIN NEMATI: Implementing the algorithms on the cloud
allows us to have an infrastructure that
can scale up and down.
If I want to monitor 100 patients versus, maybe
tomorrow, 10,000 patients, or even a million patients,
the cloud gives us the ability to not
worry about the infrastructure, and focus mostly
on improving the algorithm itself.
ASHISH SHARMA: Nurses or the physicians
can see a dashboard such as this.
SHAMIN NEMATI: At any given point in time,
the score is telling us the chances
that the patient will develop sepsis in the next six hours.
ASHISH SHARMA: So it kind of gives somebody
a high-level view, and you can quickly see or sort
patients that are deteriorating [INAUDIBLE]
the patients that are stable.
In this particular case, the algorithm
thinks there's a 0.41 risk of sepsis developing.
The top three reasons why that's going to happen
is the current temperature, the heart rate, and the chloride.
Then you go into the details of it,
and you see, OK, what were the trend lines?
Now you're talking in the language of a clinician.
You're describing what your algorithm picked out
in this vast pool of signals.
CHRIS JOSEF: A clinician, when they see a patient,
is limited really to the experience
that they personally have had, or maybe even read about.
With machine learning, what you can do is, in a matter
of seconds, compare this patient to a million, or 10 million,
and so bring to the clinician's attention things
that are worth looking at.
SHAMIN NEMATI: We are able to catch 85% of septic patients,
and at the same time, maintain a low rate of false alarm.
TIM BUCHMAN: Medicine is a very inertia-laden, risk-averse
profession.
Those of us who went to medical school,
we learned how to do it the way our teachers taught us
how to do it.
CHRIS JOSEF: The goal is not to replace the physician.
What we're doing is giving him or her resources
to make a better, more informed decision
in a very quick amount of time.
TIM BUCHMAN: This is not going to be restricted
to advising on sepsis.
It'll be advising on who needs a blood transfusion, how
quickly we can win somebody from a respirator, who's
at risk for developing an abnormal heart rhythm.
And we will learn through this process what
the optimal partnership is among the patient, family, caregiver,
and computer.
SHAMIN NEMATI: This is real.
This has the potential to help real patients.
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