>> You're not going to want to miss this episode of the AI Show.
Where Ashly talks about what's new in Text Analytics for Health.
Make sure you tune in.
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>> Hello and welcome to this episode of the AI Show.
Where Ashly's going to talk about what's
new for Text Analytics for Health.
Ashly how you doing,
my friend, it's been a little bit.
>> I'm well, how about you?
>> Great. For people who don't know,
tell us who you are and what you do.
>> Sure. My name is Ashly Yeo,
I'm a Program Manager in
the Text Analytics side of Cognitive Services.
Analytics is a service where we use
our natural language processing techniques
to process unstructured text,
for several different features such as sediment analysis,
name entity recognition, language detection,
and of course, key phrase extraction.
>> Awesome. Last time we
chatted a little bit about Text Analytics for Health.
Can you give people a little bit of a recap about what it is?
A reminder of the service and maybe a recap
of what's new since build.
>> Sure. Text Analytics for Health is one
of the new features that we will be GAing in June,
about a month from now.
It is one of the features that will use
four different natural language processing techniques to
process or extract insights from subject texts,
such as the doctors notes, discharge summaries,
lab reports, clinical documents,
as well as electronic health records.
The four techniques is available in
the Text Analytics for Health is
name entity recognition, entity linking.
We have relation extraction as well as assertion detection.
What's new is been is that we have almost
doubled the number of entities that we recognize,
as well as the relation that we extract.
>> This is amazing because it feels
like we're hiring a bunch of things
now to solve a very important problem.
>> It sure is. Every year the medical segment generates
over four trillion bytes
of the document data that we need to process.
As you've seen from the last year during the pandemic,
we realized that there was a lot of stress
and undue load on our healthcare providers.
Having to document every single encounter.
Using this NLP techniques,
we are hoping that we can reduce and we have seen some reduction
in the necessary workload of our health care providers,
thereby increasing the quality of
care to those who really need the care.
>> This is cool. Can you visualize a little bit,
help us understand what it's actually doing?
>> Sure. Let me just go through what the services
available and then I can talk a little bit.
Text Analytics for Health is a single API call that will
return four different results
that I just listed on the screen here.
This today is available as a downloadable container that you
can download and run in your own environment,
then it is a synchronous operation.
But also today we make it available through a web app API,
it's available in all regions today.
It has all the goodness of Azure with
all the compliance of security and privacy.
The privacy policies that are available for our web services.
Again, this is currently only available for English texts.
But we have customers who have been
using translator to help make that
we have stopgap for the text that we don't support
today and if seems very satisfactory results.
Today, let me just show you in the container,
we've added an application to help visualize the output.
>> That's cool. What's cool is,
as you're bringing this up,
I love the idea of there being a container that's run on-premises,
because sometimes there's sensitive data
on there and there's regulations that people need to look out for.
Is that what everyone was thinking about when they made this?
>> Yes. In fact,
container was the first version of
a Text Analytics for Health that we
released over almost a year ago,
and it's been a great hit with the customers,
and then they asked us to put on this demo UI to help
them do very quick evaluation of the data they have,
being able to see the results we get.
You can see on this screen here,
it visualizes the entities that we
detect and assign them to the different categories.
For instance, are you seeing
some of the age there, you have someone with
a diagnosis and also linked them to very important concepts,
ontologies and then those are linking and identifying
those code is really very
critical to ensuring accuracy in billing,
as well as being able to do the definition is
shared between different health care professionals,
such as when you're being referred to another specialists,
they need to know what's in
the notes and they're all talking about the same thing.
So linking is really key in making
sure that everybody speaks the same language.
Again, we also have assertion,
assertion is also something that we need to make
sure that the entities we detected,
are they hypothetical or they negated,
so that you don't assign
the wrong categories for the symptom to the patient.
What you see here is a visualization.
I can actually do a demo if you like.
>> Yeah. I'd love to see,
I love the visualization,
but there's programmers here watching.
I want to make sure that it can see what is
it that you're sending and what is it that you're getting back?
Could you have time to show us that?
>> Yes, absolutely. Let me find my screen here.
What you see here is a Visual Studio code representation
of something I put together through a Python code.
It is a simple code
using Python code to invoke the requests library,
that will allows to make a simple REST API call
to a local endpoint
that I've deployed on Azure Web for applications.
>> That's cool.
>> So you can see that we are using this API requests library.
I'm able to define
the endpoint and then provide it with a sample text,
here that we can then just simply send
or construct the call and request to the endpoint,
let me see if I can short run and hopefully it will run.
>> Here it goes. Wow.
>> As you can see, it returns a JSON object.
The output is vast because it's a lot of information behind it.
I remember one entity that you get for each entity that it
detects that has a related ontology linked to it,
it will sign all the ontologies for that.
>> That's cool. That's cool.
That's a cool REST example,
do you have any other examples?
>> Sure. I think we also have thanks to
our Visual Studio Code extension for Jupyter Notebooks,
we also have ability to do a Notebook run here.
I have a set of a simple Notebook that we
use pandas for visualizing the data,
the output that comes back from
the endpoint here is again this call,
is to a local container instance that I
have on my workstation in the office.
I'll run this and you can see the similar output.
It comes back to the JSON output.
Here using the pandas library,
we can actually visualize this output and here I
want to look at all the entities that are being extracted.
You can see a nice data by the table form of
all the different entities and
the category associated with them as well as the links,
as opposed to being a blob of JSON tags.
If I want to continue to look into
a particular category, let me go here.
For instance, I want to just only look at those with diagnosis.
I will find only those that have diagnosis in here.
This is the beauty of Jupyter Notebooks as well.
You don't have to recreate the whole visualization of the data,
but you have this dataframe they can then
use and you overlay that with UI,
you can only extract the certain areas,
certain categories that will be important from the notes
for a specialist or any person who's providing care.
Do they look in this, for instance, here,
we can look at the relationships as well,
and relationships are key.
You're looking at when, time of medication,
the dose of medication.
Those things are already inside the JSON object.
But through this dataframe,
you can actually slice and dice a little bit more.
>> That's super cool.
You showed us a REST call which
feels you're just going like the bare metal to that.
You showed us using a Notebook
with pandas dataframe, which is nice.
Do people have the option of just doing
a coded type of thing as well?
>> Absolutely. We do have an SDK that we provide.
Again, I'm using a Python SDK,
and the latest version is 5.1.0B7,
this is the one that will support what we have in
the latest review version and then wait for the GA in a month.
We'll have a GA version of the SDK.
In here we have all the different methods
and libraries that we incur.
Other will give we can call,
so using this SDK,
we can provide a client to authenticate your credentials.
Again, you do need
a text analytic resource that you can create through your portal.
Make sure you get the key and make sure you get the endpoint.
Of course if you have that,
you can then construct using this SDK,
provide it with your text
that you want to process and then make the call.
Again, please note that you went through the hosted Web API.
It is an asynchronous call,
maybe I didn't mention it early on.
It's asynchronous, you can send a lot of data to it.
A lot more data you can process to the container.
Are they that way? Because if you send,
it's asynchronous, you can come back and get the results later on.
This is what this SDK provide and I can run this again.
We will show the two calls,
the post and the get through a single method
here to begin analyze healthcare entities.
>> Let's do it. There it goes.
>> It's asynchronous, you post and it
waits for the results to get done and
then do a get and check the status for succeed, if succeeded.
It will then print and you will see
this results, it will come back.
I hope it does, it looks too busy today, but there we go.
>> There it is. That's cool.
>> This is how we can program analytically
handled the call as well as the response,
and maybe you can then structure
your response anyway you want through this SDK.
>> This is all really cool stuff.
Where can people go to find out
more about this? Because this is really cool.
>> You can have the link that you show up right now,
which I can't really see.
>> There you go.
>> TextAnalyticsForHealth/HowTo. Wonderful.
>> Yeah.
>> Then we have another link in here.
TextAnalytics/Sample as well. Tell us about the sample,
is that the one you were just showing us?
>> Yes, it is. This is the sample I just showed you through
the SDK and those all been certified to run.
>> Fantastic and looks like as
a tech community blog posts as well that you can take a look at.
Well, Ashly this has been amazing.
Thank you so much for showing us a little bit
about what's new with Text Analytics for Health.
Thank you so much for watching my friend,
this has been another episode of the AI Show,
we're learning about again,
what's new in Text Analytics for Health.
Thank you so much for watching and hopefully
we'll see you next time. Take care my friends.
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