Learn from AWS Healthcare solution architects how to ingest data from live streams (like glucometers) and a data extract source. Use these to build a patient cohort, based on various criteria like lab values, demographics, and other clinical events. From that cohort, you can learn how to run predictive models using machine learning on AWS. For the workshop, you will be divided into groups of 5-7 participants. We will be using an AWS IOT button to act as a glucometer that sends lab readings to the cloud. Then use AWS Glue to merge these readings with existing clinical records and transform the dataset for machine learning. Finally, we will use a machine learning model running on AWS deep learning AMI to generate predictions. To make sure you get the maximum value out of this workshop and enjoy building on AWS, we wanted to share with you a few prerequisites that will help you get up and running quickly on the day of the workshop: This will be a hands-on session where you will be accessing various AWS services. Make sure you have an AWS account and have access to login as an IAM user with admin privileges. If you do not, we recommend signing up for a new AWS account before you come for the workshop. We will be using an AWS IOT button during the workshop. If you have an IOT button with you, please get it along. If you don’t, no worries. We will have a few buttons to be shared within the group which the participants can take turns to try out. Please download a MySQL database client like MySql Workbench to run scripts on the database. The code for the workshop is written in python. Make sure you have your favorite python editor to view code. You will not be required to write new code, just modify existing code with some parameters. Important points: The workshop is intended to provide you with hands-on experience of building solutions on AWS. We will provide you workshop guides that give you details of how to build the various components of the soliton. However, it will be good to have some previous AWS experience so you can work through the steps faster. Please also leverage the knowledge of others in the group during the workshop. There will be assistants ready to help you if you get stuck. We will time box each section of the workshop so we are able to complete the end to end solution in time. If you are not able to finish a particular section, don’t worry. We will provide you script that will accelerate you to the next step of the solution. Last and the most important point: we want you to have fun while working together in groups. Please let your assistants know how we can make this experience better for you.

1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
AWS re:INVENT
Transform Chronic Conditions
Analysis with AWS: Stream, Cohort,
and Machine Learning
U j j w a l R a t a n a n d N a v n e e t S r i v a s t a v a
H L C 3 0 3
N o v e m b e r 2 7 , 2 0 1 7

2. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Problem Statement
Help identify and predict cohorts of chronic diabetes patients who
may encounter a kidney related problem.

3. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Why Is This Important?
• Diabetes mellitus, usually called diabetes, is a disease in which the body does not
make enough insulin, thereby increasing blood sugar levels.
• About 30 percent of patients with Type 1 (juvenile onset) diabetes and 10 to 40
percent of those with Type 2 (adult onset) diabetes eventually will suffer from
kidney failure.**
• Our goal is to find the indicators that drives high chances of getting kidney failure.
• This will allow targeted interventions in patients who demonstrate these indicators
to reduce the risk of kidney problems.
**Reference : https://www.kidney.org/atoz/content/diabetes

4. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
The Business Flow
Identify case and
control groups
Gather data from
Glucometer
Transform
data
Explore Features and
Apply deep learning
Analyze and
visualize the data
Patient
Dataset
Intervention
Drive Changes

5. The Technical Flow
Clinical
Records
Glucometer
(IoT Button)
AWS IoT ingests lab
values from the
glucometer
Integrated data in
Amazon RDS
AWS Glue for
transforming data
Transformed data
aggregated in Amazon
S3 to build a data lake
Transformed data in
Amazon RDS for
machine learning
Batch
Stream
Deep learning AMI
for training and
testing the machine
learning model
The Machine Learning
Model predicts if a
particular patient has
kidney disease or not

6. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Workshop Logistics
Prerequisites:
• Laptop with wireless internet connectivity and browser.
• AWS account, IAM user with Admin privileges.
• MySQL Database client like MySQL Workbench
• If you have an Android/IOS device, download the app “AWS IoT Button Dev”
• Code editor for python to view and modify code
• Familiarity with AWS services like CloudFormation, Lambda, RDS and EC2. Experience working with notebooks (like
Jupyter, Zepplin) is a plus.
Note:
• Time boxed Labs with instructions to help you get up and running
• You are divided into groups so help each other complete the labs. Help available in the room if needed
• IoT buttons are limited. Please share
• Have fun building on AWS!
Go to http://bit.ly/2ir4OyC to begin. Unzip the downloaded file which will create a new folder
diabetes_workshop. Open the file “1_Setting up the workshop.docx”

7. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Appendix

8. AWS IoT

9. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Data Integration: Challenges
Distributed sources
Data conformation and transformation
Data quality and profiling
Data pipelines and access/security

10. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Why Transform
The quality of the ML model is dependent on the data used to train.
• Clean data
• More data (more rows) means more accurate predictions
• More attributes (columns) means more patterns and relationship within the data can be
discovered
So, Transform Data to
• Filter out Uninformative Data
• Change Records With Feature Engineering
• Complete Missing values
Remember ……ML models are only as good as the data

11. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
AWS
Glue
Managed Transform Engine
Job Scheduler
Data Catalog
Built on Apache Spark
Integrated with S3, RDS, Redshift &
any JDBC-compliant data store

12. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Glue Automates the undifferentiated
heavy lifting of ETL
 Cataloging data sources
 Identifying data formats and data types
 Generating Extract, Transform, Load code
 Executing ETL jobs; managing dependencies
 Handling errors
 Managing and scaling resources

13. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
AWS Glue Components
Data Catalog
 Hive Metastore compatible with enhanced functionality
 Crawlers automatically extracts metadata and creates tables
 Integrated with Amazon Athena, Amazon Redshift Spectrum
Job Execution
 Run jobs on a serverless Spark platform
 Provides flexible scheduling
 Handles dependency resolution, monitoring and alerting
Job Authoring
 Auto-generates ETL code
 Build on open frameworks – Python and Spark
 Developer-centric – editing, debugging, sharing

14. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Consolidation of data into a Datalake
Data
scientists
Automation /
events
Business
users
Data
analysts
Engagement
platforms
1. More personas need access to data, through appropriate tools
2. More systems need to link to data for decision and process automation
3. Users need to be able to find information, and access it securely

15. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Expanded Architecture
Speed (Real-time)
Ingest ServingData
sources
Scale (Batch)
Data Warehouse
Amazon Redshift
Legacy Apps
Amazon RDS
Data analysts
Data scientists
Business users
Engagement platforms
Schemaless
Amazon ElasticSearch
Direct Query
Amazon Athena
Near-Zero Latency
Amazon DynamoDB
Automation / events
Amazon S3
Staged Data
(Data Lake)
Semi/Unstructured
Amazon EMR
Transactions
Web logs /
cookies
ERP
AWS Database
Migration
AWS Direct
Connect
Internet
Interfaces
Amazon
Kinesis
Connected
devices
Social media
Amazon S3
Raw Data Amazon EMR/ Glue
ETL
Advanced
Analytics
MLlib
Event Capture
Amazon Kinesis
Stream Analysis
Amazon EMR

16. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Machine Learning
Machine learning (ML) can help you use historical data to make better business decisions.
ML algorithms discover patterns in data and construct predictive models using these patterns.
Features engineering is a key step in machine learning.
Models are used to make predictions on future data.
Workshop: Predict kidney disease in diabetics.

17. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Models - Train , Test , and predict
A training dataset is a dataset of examples used for learning, that is to fit the parameters
A test dataset is a dataset that is independent of the training dataset, but that follows the same probability
distribution as the training dataset.
The training dataset often consist of pairs of an input vector and the corresponding answer vector or scalar,
which is commonly denoted as the target.

18. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Notebook documents are both human-readable documents
containing the analysis description and the results (figures, tables,
etc..) as well as executable documents which can be run to perform
data analysis.
Pandas is an open source, BSD-licensed library providing high-
performance, easy-to-use data structures and data analysis tools
for the Python programming language.
Deep learning denotes the modern incarnation of neural networks.
With distributed cloud computing and parallelism across GPU cores,
we can train models millions of times faster.
MXNet provides optimized numerical computation for GPUs and
distributed ecosystems, from the comfort of high-level
environments like Python and R
Deep Learning Tools

19. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Jupyter Notebook – Setup and Tunneling

20. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Jupyter Notebook – Pymysql - Database

21. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Jupyter Notebook – Grouping and
Aggregations

22. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Jupyter Notebook – Restrictions ,
Aggregations and Histograms

23. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Jupyter Notebook – Modelling

24. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
THANK YOU!
C L I C K T O A D D T E X T
C L I C K T O A D D T E X T