TASSALLAH ABDULLAHI: Thank you.
I'll start by saying thank you, everyone, for being here today.
I want us to begin by envisioning
a scenario where one falls ill.
As is customary, we visit the hospital
and relay the symptoms of our condition
to a doctor with the hopes that they can pinpoint
the ailment afflicting us.
But the process of diagnosis is anything but simple.
This doctor has to make a prediction,
a correct prediction, from a plethora of possibilities.
Also, keep in mind that several diseases have similar symptoms.
I can count off the top of my head
how many diseases share fever or chest pain as a symptom.
Now, studies have shown that a single doctor has
a mere 62.5% chance of accurately predicting
the correct diagnosis, but this accuracy
increases when multiple doctors work together
in diagnosing a single patient.
But assembling multiple doctors for a single patient
is not always feasible, and expensive.
That is why computer science researchers
like myself have decided to come up with tools, specifically
computerized tools, to computerize diagnostic decision
support systems.
But why is this pursuit of paramount importance?
Unfortunately, diagnostic errors is a leading cause of death.
Shockingly, over 18 million diagnostic errors
are made each year, and over 74,000 deaths
are attributed to these errors.
Misdiagnosis is even more likely to occur
among patients with uncommon diseases,
diseases that could be rare, or diseases
that don't occur frequently.
Now, what's currently being done about this
in the field of computing?
Researchers has decided to come up with machine learning tools
to aid and supplement doctors in this process of diagnosis.
The most popular and widely used method
is the supervised machine learning,
which necessitates lots of labeled data.
So what does this mean?
Say, for instance, I want to train a model
to be able to describe and identify dog from images.
The first step is to prepare labeled examples
that tells this model what a dog is and what a dog is not.
Then we train this model with labeled examples,
and then the next step is to test
this model with some unlabeled examples
to evaluate if this model has learned the task correctly.
Now, when we have lots of labeled data,
supervised machine learning model is desirable.
In fact, it's very effective.
Even humans learn effectively through examples.
But in the case of diseases, this
labeled examples are not always available,
and challenging to find, especially
in the case of rare diseases.
That has led to the scarcity of supervised models
for this condition, and even when they do exist,
they are often disease specific.
Imagine having different disease models
for different diseases in your hospital settings,
where we have to consider thousands of possibilities.
Imagine-- look at this poor doctor.
He doesn't even know where to begin.
So my research goal is to build a single generalist model that
performs well for a wide range of diseases that includes
rare and uncommon diseases.
Specifically, the task I aim to solve
is, given a patient's clinical note that contains a patient's
medical history, I want the model
to be able to return a ranked list of probable diagnoses.
So the goal here is to reduce the search
space of possible thousands of diseases to a likely few.
To achieve this, we created CliniqIR.
CliniqIR is a fully unsupervised model.
Fully unsupervised here means the model doesn't
rely on labeled examples.
Rather, it relies only on published,
peer reviewed medical articles to perform
literature guided predictions.
So to evaluate the effectiveness of CliniqIR,
we had to test it on a popular data set,
a popular disease data set called the DC3 data set.
So the DC3 data set contains 30 complex, unique, difficult
to diagnose disease cases that lacks labeled examples.
So research previously hasn't focused on those data sets
because it lacks labeled data, so we
compared CliniqIR's performance on that data set
with other biomedical models.
And our result here shows that CliniqIR, which is in red,
clearly performs better.
This result demonstrates that it's
possible to achieve a single model that
predicts a wide range of diseases
that could be frequent, difficult to diagnose,
and complex.
This results holds the promise of a tool that aids doctors
in the process of diagnosis, thereby
improving patient outcome.
Ultimately, this result brings the promise
of mitigating diagnostic errors and the consequential patient
fatalities.
With this, I hope you all understand
why my research matters.
Thank you.