SPEAKER: Thanks to machine learning,
we can extract knowledge from medical records, call center
conversations, medical voice soundbites, medical forms,
regulatory filings, research reports, insurance claims,
pharmaceutical documentation, and more.
This ultimately helps doctors and care teams
get holistic views of their patients quickly,
or health plans to see population trends
for their members, or pharma to draw insights
from drug development research.
This is possible thanks to a field known as natural language
processing, which is concerned with programming computers
to process and analyze large bodies of human communication
that can live in many different formats,
such as written texts, spoken utterances, or even
official documentation.
And so in this episode, we will share how organizations
can use one of Google's natural language services
to specifically help process structured
and unstructured health care language
data using NLP, which stands for Natural Language Processing.
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The Healthcare Natural Language API
contains four key features that help you find, assess, and link
knowledge in your data in the following ways.
For one, it has the ability to text
to medical concepts, which is referred
to as knowledge extraction.
It also identifies and connects related medical attributes,
which is known as relation extraction.
It can also assess surrounding factors
that could be clinically relevant,
known as context assessment, and standardizes medical concepts
so they can be analyzed across systems,
known as knowledge linking.
Another way of thinking about NLP
is that it can extract critical clinical information,
like medications, medical conditions,
as well as understand contexts, like negation,
such as, this patient does not have diabetes.
It also understands temporality, such as, this patient will
start chemotherapy tomorrow, and even infer there
are relationships between things,
such as side effects or medication dosage.
And what's most notable is we have
a long list of ontologies the natural language
models are trained with.
Two notable ones are the ICD, which
is used to code and classify morbidity data from inpatient
and outpatient records, physician offices, and most
National Center for Health Statistics surveys.
There's also SNOMED clinical terms,
which provides core terminology of electronic health records.
The models also include US official codes
for insurance procedures and RxNorm,
which contains a list of all medications available.
Technical practitioners can leverage healthcare NLP
to build apps for their own organization
or for their entire industry, such as enabling precise search
and discoverability across patient populations,
enable exchange of digital health care information
and avoid processing delays, establish and maintain
regulatory compliance, or automate
administrative workflows, such as identifying and eliminating
errors that require corrective activities.
Anyone who has science-based texts around research
or clinical data can get value from our Healthcare NLP API.
Here are a few sectors that can benefit to name a few.
In telehealth, you now have off-the-shelf support
for exchanging medical knowledge captured in written form
and can extract structured knowledge from texts
and make it available for digital services,
such as chat bots, call centers, clinical decision support
systems.
This also frees up time by triaging patient calls
and resolving cases that do not require the intervention
of a clinical professional.
Pharmaceutical researchers are also
enabled via a standard patient discovery
interface for population health and R&D applications,
since papers and clinical trial documentation
can be surfaced to match patients or find
novel treatments.
Those managing clinical trials can
both increase their number of participants,
as well as process the high volume of feedback
and decrease time to government approval.
Users who manage billing, especially
in insurance companies, can have even better integration
with claims payment or automate billing and coding
for insurance.
You can enable the Healthcare NLP
from your Google Cloud Projects UI or via the command line.
If you do not have a project, there's
a link in the description along with other helpful resources.
And once you have setup permissions
you can begin using its context-aware models
to extract medical entities, relations,
and contextual attributes.
Each text entity is extracted into a medical dictionary
entry.
To extract medical, texts make a POST request
and specify the following information in the request.
First, the name of the parent service,
including the project ID and location.
Also, the target text.
The maximum size is 10,000 Unicode characters
at this time.
Let's quickly show you how you can leverage the Natural
Language Healthcare API.
We built a demo application with a JavaScript frontend
to visually understand the output of the Healthcare NLP
API.
Let's test out the sample medical record
for a hospital patient.
We send over the sample medical tests
to the API on the backend, and we render the JSON response
in this web app.
In the first panel down here, we can
see the various entities extracted
and their corresponding medical code.
Next, we can see their diagnoses with their corresponding
confidence scores.
Now looking at the relationships between the entities,
we can actually group together important attributes.
For example, take a look at how long
and how much of these medications
are taken or were prescribed.
There are so many possibilities when using Healthcare NLP,
and especially when you pair it with Google services,
such as Dialogflow AI for a chatbot interface.
You can even build custom models to build low-code apps using
AutoML Entity Extraction for Healthcare,
so that users can simply upload documents and then
perform manual annotations to train and build a model on what
they would like to extract.
This can then be integrated into larger data pipelines.
You can also use Document AI to process faxed documents
or enable enterprise search for life science
organizations using the Google Knowledge Graph.
To learn more about the Healthcare Natural Language
API, you can visit cloud.google.com
/healthercare/do cs/concepts/nlp.
And community, if you found this episode helpful,
please subscribe to the channel to get notifications
of more health care episodes.
Cheers.
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