Welcome to 'This is My Architecture.'
I am Hawn, and today I am joined by Kanti from Fresenius.
Welcome. Good to be here.
So before we get into your architecture, can you tell us a little bit about Fresenius?
Sure.
Fresenius Medical Care is a premier health care company
that's providing high quality care to more than 200,000 patients
living with chronic kidney disease, through innovative solutions,
that's redefining healthcare and setting industry standards.
Can you tell us a little bit about one of your innovations?
Sure.
So intradialytic hypotension is the most frequent complications
happening during hemodialysis.
It might result in unconsciousness, hospitalization, or other complications.
Management of intradialytic hypotension incidents
require a substantial amount of staff attention,
that might increase the treatment cost.
This architecture enables us personalized care for each and every patient,
which can reduce and also track the intradialytic incidents
sooner through predictive models.
Wow, so let's dive in.
Sure.
So here we have our clinics, where we have our dialysis devices,
which are used for hemodialysis.
With the breakthrough of technology, we now have access to the data from these machines,
which are transmitted to Kafka topics.
So what are you using the Kafka topics for?
So Kafka here plays a key role to stage all the messages
coming from the clinics every 10 seconds.
And this data sets the machine payloads,
are also used by other applications within our ecosystem.
I can see that Kinesis Data Stream is one of the consuming apps.
That's right.
What is it used for?
So Kinesis Data Stream is using the data from the Kafka topics,
through kinases, Kafka connector and processing that to Lambda.
Lambda then pick up those messages and processes to Kinesis Data Analytics,
and then Kinesis Firehose.
For the Kinesis Data Analytics, what are you leveraging that for?
That's a great question.
So Kinesis Data Analytics plays a key role here
to build our features for our intradialytic hypertension model.
The Kinesis Data Analytics builds the dynamic features
in near real-time on the machine data,
as well as it integrates with the static data
coming from the clinical data sets to get the complete list of features.
Once the complete list of features are ready,
it then sends lambda dent in, gets those list of features
and sends to SageMaker to get the inference results out of SageMaker.
So from that, that predictive modeling, like who visualizes that?
So the SageMaker results, the inference results here
are then sent back to Elasticsearch,
where we are building visualization through Kibana,
for our user groups, who can look at a patient's probability,
probability whether the patient is about to go to intradialytic hypertension.
So they're able to visualize that near real-time.
That's right.
That's awesome.
I also notice that you use Kinesis Data Firehose as well.
So, what is the purpose for that?
So Kinesis Data Firehose is playing a key role
towards building our big data lake.
So we are ingesting all the data,
machine data flowing in near real-time
to S3 for our Big Data Lake storage
for any analytics, advanced analytics and machine learning use cases.
Gotcha.
It looks like there's a data center there,
what are you pulling from there?
That's a great question.
So the data center here, we have our enterprise data warehouse,
which have all the clinical data sets, which are sitting there.
These clinical data sets are also part of our intradialytic hypotension features.
So Glue helps us extract the clinical data sets
from data warehouse and pushes that to S3.
Once the clinical data sets are pushed to S3,
we are also building our static features through Glue and storing it into S3,
which is then used by Kinesis Data Analytics
to get the complete list of features for our intradialytic hypertension model.
Thank you for going over your architecture.
That is amazing how you're able to capture the near-real time patient dialysis,
so that we can predict and provide that patient care,
but also be able to capture the data models
for more predictive and better insights
for even further patient care as well.
So thank you for that.
Sure, thank you.
And thank you for watching 'This My Architecture.'