- Today, I'd like to introduce our first keynote speaker,
Dr. Kira Radinsky.
Kira is the Chairperson and CTO of Diagnostic Robotics.
She was named one of Forbes Under 30 Rising Stars
in Enterprise Tech.
Dr. Radinsky gained international recognition for her work
at Technion and Microsoft research for developing predictive
algorithms that recognize the early warning signs
of global events, such as disease epidemics,
and political unrest.
Welcome Kira.
- Hi everybody.
I'm Dr. Kira Radinsky.
I'm a visiting professor in machine learning in healthcare
at the Technion.
And what I've been doing for the last few years
is building systems that are capable of taking everything
humanity wrote since 1851 till today,
and build machine learning models that can read
all this information and predict future events,
such as pandemics,
but also personalized predictions in healthcare.
For the last few years I've been obtaining data
about 150 years of news articles, billions of tweets,
millions of web searches
and started building a system that can read this
information, extract causality from it to predict events.
How did it work?
We would gather information from sentences,
like in this example, tanker and merchant ship collide
and apply natural language processing to identify
who've done the action, what was the action
and what was performed and with what.
The natural language processing systems was trained
with different statistical algorithms.
We then build causality identification algorithms.
It would actually look for cues that we humans use
to convey to each other around causality.
For example, one would write that this caused another event,
oil spill of Singapore.
And then we created a graph of 300 million nodes
and billions of edges representing everything
that humanity knows about causality.
We then started working,
trying to apply this with different collaboration,
for example, here, this was during my work
in Microsoft Research with collaboration
with the Gates Foundation,
we were tasked to predict Ebola outbreaks.
Unfortunately or fortunately, Ebola
didn't happen many times in humans,
but it did happen many times in animals.
And what is very interesting that
in all of those cases in animals,
they all started from a single event
when humans were looking for diamonds and gold,
the system knew that when people were looking
for diamonds and gold,
deforestation is about to happen.
The system knows that when deforestation happens,
animals gonna migrate,
but specifically one type of animal, the bat.
And indeed the latest Ebola outbreak started
from a three year old child who ate an uncooked bat
bush meat killing more than 2,500 people.
But only the line on causality,
which humanity already knows about is not sufficient.
We would actually try to identify new phenomenon
in scientific discoveries.
For this, we started combining the information
from the causal infrastructure,
with the correlation infrastructure.
For example, when tasked with the prediction
of trying to predict cholera outbreaks,
the system already knew that storms and floods
might cause cholera.
The reason for this is that cholera is a waterborne disease,
but we all know that not all floods,
are gonna cause cholera.
What is so unique about those floods and storms?
We started running the system,
trying to identify correlations and tasks
to identify what's unique about those storms
and system identified that if you have a drought,
two years before those storms, the probability
of them causing cholera is significantly high.
It was caused by six cases in 2006, till today
of cholera outbreaks in Angola.
All of them had this pattern.
In Bangladesh since 1960, till today,
19 significant cases of cholera.
84% of them had this pattern,
but this pattern doesn't happen anywhere else in the world.
It doesn't happen in Tel-Aviv.
It doesn't happen in New York.
So what is so special about Bangladesh and Angola?
You're probably thinking to yourself that
this is probably something around the GDP
of those countries,
and I'm gonna ask you, how do you know,
what is the GDP of those countries?
You probably didn't read about this
any time recently, right?
So this is exactly the concept of word knowledge.
There's so much things that we already know
that enable us to abstract and understand
what's so unique about Bangladesh and Angola,
that algorithms just look at correlation
and even causal infrastructures and information coming
from world knowledge is not enough.
So we gave the system access to hundreds
of different data sets.
Some of them are structured. Some of them are not.
The larger one is Wikipedia.
All of them coming from Wikipedia data stats,
and let the system think about different types
of abstractions, trying to identify which one
is statistically significant
when concerning about this specific pattern.
Is it because of the area total ethnic groups,
pollution intensity, government type?
The system identified that only two parameters
actually mattered.
The GDP and the percentage of water.
GDP is not surprising.
Many of those countries with low GDP
have bad sewage systems,
so higher chances of this type of epidemic to actually grow.
But what a surprise is that fact
that the system identified that countries
that have a low percentage of water
have a higher probability of this pattern.
And the reason this is surprising is that
this is a waterborne disease.
So we would actually expect this to happen in countries
where they have a higher percentage of water.
In April, 2011, our system reads a BBC article
about a drought in Cuba and start alerting
about the possibility of a cholera outbreak in two years.
We completely ignored this at this time,
we're still in the development stage of the algorithm,
and frankly, Cuba didn't have any cholera outbreak
in the last 130 years.
But then year and a half later,
the system picks up another news article from the Reuters
about tropical storms coming next to Cuba,
this time, giving a very high probability for cholera.
We started talking with different aid organizations
and indeed a month and a half later,
the first cholera outbreak in Cuba in 130 years.
Cholera is a disease that kills 100,000 people every year.
But it's very simple to treat.
If you get clean water in time,
mortality rates dropped from 50% to less than one.
So all of those are re-organizations actually needed
is not even two years in advance.
Two to three months was actually enough.
So what we use the system is for identifying that something
in the weather is actually correlative or causative
of cholera outbreaks.
So we obtained access to much more granular data
coming from sensors of weather
from all of the airports since 1970
on an hourly basis.
We combine this with information about any cholera outbreak
that the World Health Organization had
and started building machine learning models
that are capable of predicting based on weather data
what's the probability of cholera in those locations
in the next two or three months.
And we did a very interesting pattern that looks
at extreme dryness, changing to wetness immediately,
which is highly indicative with a 94% accuracy
before a cholera outbreak.
But everything I was telling you right now
is around predicting large epidemics and pandemics.
And what we're started doing in Diagnostic Robotics
is trying to understand how we actually handle
something extremely targeted
like the waiting times inside emergency departments.
Till 2030, we're gonna have 3.8 billion people
without access to primary care.
So the three hours of waiting right now
inside the emergency department is gonna be nothing
compared to what we're waiting right now.
It's gonna be simply eight hours and more like people
are waiting today in China.
So we've obtained access to billions of medical visits.
We build a natural language system
that can read all of those medical summaries,
extracting answers to clinical triage questions,
through an automated question answering system
and correlating it with the diagnosis that a doctor gave,
which task it was given et cetera.
And we started different prospective trials.
We ran with 75% of the HMO's in Israel
with five of the largest hospitals.
And with prospective trials in Mayo Clinic
(indiscernible) in United States as well.
The way the system worked as well,
patients were waiting in triage
inside emergency departments.
They'd been asked to come to what we call the AI stations,
asked a set of clinical questions by the system
like a doctor would,
predict behind the scene which task they need,
how urgent they are and start building
navigation inside the hospital.
We then understood that actually handling patients
when inside emergency department is just too late.
70% of people inside emergency department
could have been handled in primary care.
This is saving a lot of stress for the patients
and even more than $200 billion
for the healthcare system as well.
So what we started doing is integrating
through different entry points
in the healthcare primary care systems.
While patients were scheduling an appointment,
a system would interject this glow,
ask a set of clinical questions.
Does it hurt when urinating?
Do you have any lower abdominal pain?
Behind the scene, it would actually reach a probability
based on billions of medical visits.
60% of this is UTI.
The health system doctors wrote a rule
that everything above 30% should do a urine test
before meeting with the doctor.
So the system would actually navigate them to a urine test,
either in a lab or in one of our trials
to actually being handled a sensor to your home
in an SLA of three hours.
You do the urine test at home, take a picture.
A system would read this from the medical record,
update a probability of UTI to 90%,
everything around 85% and up,
send a Wi-Fi to the primary care physician,
who can approve antibiotics remotely
with an additional SLA of three hours
till reaching the patient.
So we're talking here about the digital interventions
and digital infrastructure for digital health care.
So we're deploying this with lots of the health plans,
both in the United States and in Israel,
even in Singapore initially,
and then COVID happened and we were requested
by the Minister of Health in Israel
to extend the digital triage system.
This time, triaging for all of the 8 million patients
in Israel, all of the HMO's and all of the hospitals.
The way the system work is we will produce
for the government a hit map of where COVID is right now.
But most importantly, predicting
how it's gonna spread in the next few days.
The way it was used is in the first wave,
we needed to know where to send COVID tests in time,
because the belief was this time
that it would actually stop definite infection chains.
How did we do it?
Every time a person needed interaction
with the healthcare system or with the government
in any way, additional anonymous questions would be sent
to all the population.
People would be asked a set of clinical triage questions.
This was the beginning of March.
We still knew very little about COVID and some of this,
we were asking the patients to describe their symptoms
in natural language.
Behind this team in three days, we build the machine
learning model predicting from the symptoms
what is the probability
of the fact that a patient has COVID?
If not anonymized, a batch flag would be sent
to their primary care physician or nurse
that would call them and actually help
direct them in the health care system
through either isolation, emergency department, or at home.
The way the system works is either
you would get a text message from your primary care,
or you're just worried.
You were trying to contact your doctor.
All of them would lead to the same question here.
If the system predicts that
Algo is going to get an all clear recommendation,
if not, a notification is being sent to your doctor
and you're being requested to stand still
and wait for the phone call.
We've also been building models
about what's the probability of this patient to deteriorate.
The reason we needed this model
is patients with high probability of deterioration
needed to be sent immediately to the nursing department
while patients with low probability would be kept
in what we call COVID hotels or even at home.
What it actually helped us is increase the adherence
of the public to the health guidelines
for the health system introduced a lot of the waiting times,
but also moving triage to online platforms
actually started protecting them
and having less departments getting closed
because of a single healthcare personnel getting sick,
but most important for the governmental agency,
it allowed them to identify quarantine decisions,
discuss exit strategy into relief economy.
25% of the population of Israel answer this questionnaire
at least once a week,
83 to 89% completion rates.
What we would provide for the Minister of Health
on a daily basis is for each city
what is the probability that people gonna report
a positive self-report COVID.
The numbers in orange, you see,
this is what the government knew already.
And in green, this is our prediction
about how many more symptomatic patients.
As you see, this city went into quarantine.
This city, many people reported
they've done a private COVID test.
The Ministry of Health had no information about any COVID
tests at that point of time,
and the system also verified that this people
are actually symptomatic.
On April 4th, we were allowed once to publish
our predictions around which cities
have the highest probability of COVID.
We published three cities,
Migdal HaEmek, Tiberias, and Ashkelon.
The government sent COVID test, verified the results.
And since then on a daily basis,
the Ministry of Health has been deciding
about where to send the COVID tests
based on this type of predictions.
We then started trying to identify event causality.
Is COVID test is preventing infection rates,
or is it actually just additional numbers
that are trying to make us believe
that COVID is actually happening?
So we've actually done an experiment,
and this is the story of two cities.
In orange, you see the symptomatic lowest
predicted by the system.
The system here started learning
about the highest symptomatic load.
In blue, you see how many COVID tests were sent.
You see the government has sent very little COVID tests.
Here on the other hand, in our prediction,
they increase the number of COVID tests
and it flattened the symptomatic curve
as opposed to here which started growing exponentially.
So we can actually infer that
if you send a COVID test in time,
it can actually have a high probability of flattening
the curve of the number of symptomatic patients.
What the system has also allowed us to do is
identify new symptoms.
At the beginning of March, it was only known that fever
and cough are the symptoms of COVID.
A symptom identified as the loss of sense of smell
and taste after running the system less than three days
and immediately deploying this triaging system,
for the entire population of Israel.
So when a person had a loss of sense of smell and taste,
our system would predict that there
the high probability of COVID.
Our system also predicts the combinations of those.
So for example, you all know that fever and cough
are probably equated with COVID,
but our system identified that extreme fatigue
combined with dry cough, it's extremely equalitive.
And this is something that is very little about it
was published in the literature.
So there's still a lot that we can do
beyond machine learning in these pandemic times.
But we need also to remember is what's gonna happen
after the pandemic?
We've been deploying the system
and the Ministry of Health in Israel, in India
and the State of Rhode Island,
where we're giving them proactive medical response,
customized protocols and of course,
dashboards trying to tell them
when the second wave is coming,
where to send a COVID test
and to date also for differential isolation.
But again, what's gonna happen after COVID?
I think digital healthcare is gonna change significantly
and we need to understand is that today,
there's a shortage of 100,000 physicians
in the US alone.
So we cannot continue just training with doctors.
We need to start automating some of their routine work.
And we've seen this in this pandemic times,
where actually the chronic patients
that don't have COVID are the ones who suffer the most.
So this is where I think that infrastructure based
on machine learning that can help identify
differential diagnosis, urgency,
which tests to send the patient is acute
in the next era after COVID.
Thank you very much.