Key Takeaways: Challenges of Healthcare companies when moving to cloud to enable data science Common cloud data platforms for various Healthcare operations Best practices for productionizing ML using data frameworks at scale (Spark, Tensorflow, and more) Real world Life Sciences example using deep learning to predict molecular activity Webinar Abstract: In this webinar, we will be discussing how healthcare companies are modernizing their data platforms and using cloud to help break down data silos enabling innovation with data science. We will cover common cloud operations for ML and AI use cases in healthcare, highlighting several examples in different domains (pharma, life sciences, biotech, and provider services). We will share best practices of proper security and governance when migrating from on-prem to cloud data lake and the value it is helping drive for Qubole customers. Ending with a real-world deep learning example using Merck’s Kaggle competition dataset; where we leverage Tensorflow, Keras, and automated Spark cluster on Qubole’s Notebook to predict molecular activity from numeric descriptors of chemical structure.

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Modernizing AI & ML Operations to
Rapidly Advance Healthcare

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INTRODUCTION
OVERVIEW OF STATE OF DATA SCIENCE IN HEALTHCARE
● COMMON CLOUD DATA OPERATIONS
● PITFALLS TO AVOID
CUSTOMER DATA SCIENCE EXAMPLES ACROSS VERTICALS
● CASE STUDIES:
○ SERVICE PROVIDERS
○ ROBOTICS
○ DRUG DISCOVERY
DEMO OF DEEP LEARNING FOR DRUG DISCOVERY
● REVIEW ML METHODOLOGY USED
● MOLECULE PREDICTIONS FROM TENSORFLOW

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Birth of Data Science in Life Sciences
Cholera Outbreak in 1854; London
- Prevailing Theory: Miasma Theory (Cholera was caused by bad air)
- Dr John Snow refuted Miasma Theory and came up with an idea to mark on a map of London the locations of all known
cases of death caused by Cholera. This marked the birth of “Epidemiology”
- Reference: The Ghost Map by Steven Johnson

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We Are Experiencing a Shift in Growth
➔ Growth rate at All Time Low
➔ Life Expectancy at All Time High
➔ Human data growing 10x faster
than business data (IDE Study)

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The transformational promise of
Data projects remains elusive
85%of Big Data
projects fail to
meet expectations
>70%of Analytics
potential value is
unrealised
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Big Data Use Cases in Healthcare
BioTech
➔ Robotics (e.g. surgical
devices)
➔ Human microbiome
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Pharma
➔ Drug discovery
➔ Supply chain
distribution
Life Sciences
➔ RNA Sequencing
➔ Genomics
(PheWAS/GWAS)
Providers
➔ Patient treatment
➔ Doctor & Insurance
matching

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Data Ingest &
Preparation
Governance &
Security
Model Build Deploy & Monitor
Tasks: wrangling,
exploration, validation
Tasks: split data, model
specification, feature
selection
Tasks: Train, Visualize,
compare / choose models,
model report
Tasks: build, compile/JAR,
reporting dashboard,
monitor

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Convergence of Data Science
Operations in Healthcare -
Information and
compute power is
rapidly growing day
by day
Machine Learning
has become more
accessible than
ever
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Enable self-service
data analytics for
bottoms-up use
cases

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Survival Means Embracing Change

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ETL, Ad Hoc
Modern Healthcare Big Data Operations
Apache Airflow
Device
Sensors
Historical
Behavior
Genomic
Data
Client
Information
Stream

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AUTOSCALING BIG DATA ENGINES IN CLOUD

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ON-PREMISE DATA SCIENCE APPROACH VS. CLOUD
• Impossible to scale storage without scaling
compute leading to expensive deployments
• Difficult to share HDFS data across Operating
Units
• Compute & Storage Separate
• Data is easily shared across Operating Units &
accessed from different locations
Cloud
Object
Store
DATA LOCALITY NO DATA LOCALITY
Higher Upfront Cost
No Autoscaling
Having to Fit Data
in Fixed
Infrastructure
Fewer Analytics
Tools
Lower Cost
More Iterative
Scalable with
Automation
Fast Data and ML
Tool Access

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PITFALLS TO AVOID WHEN STARTING DATA SCIENCE
Over Building
vs. Delivering
Use services that save
time and money
upfront. Avoid risk of
internal development
delays
Keeping Users
Siloed from Data
Allow users to have
access to data and
analytics, in order to
enable collaboration
Data Governance
& Security
Successful Data Lakes
focus on proper data
policies and
anonymization to
ensure security

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COMPLIANCE IS POSSIBLE WITH DATA SERVICES
AVRO,
Text,
etc.
AVRO,
Parquet
Raw
(Unstructured)
Derived
(Data Governance)
Source of Truth
(Semi-Structured)
PARQUET,
ORC
Data
Services
Insert/Update/Delete
Export CSV JSON
Relational Data
Warehouses
Data Marts
(Databases,
NoSQL, etc.)
TensorFlow
Deep Learning
Use
Cases
Analytics
(i.e. Historical
Analytics, BI, User
insights etc.)
Data Sources
(i.e., Genomics, drug
behavior, patient data
etc.)
Data Science
(i.e. Time-series
Analysis, Research, etc.)
Data Discovery
Keras
Notebook
Cloud
Compute
Object
Storage
ELT
ETL

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Data Science Workflow - Team Data Science Process
(TDSP)

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How did they do it?
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6
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POLL QUESTION

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ENABLING ADVANCED ANALYTICS TO HEALTHCARE
Personas Consumers Use Cases Engines Cloud
Data Engineering
Data Science
Data Analysts
Clinical History
Clinical Trials
Drug Behavior
Insurance Payer
EMR/EHR Data
Spark
Hive
AWSPharma &
Life Sciences
Companies
Insurance Providers
SOLUTION MOVING TO QUBOLE & CLOUD
● Needed a technology to blend multiple
datasets from pharma & life sciences
companies in order to provide in-depth
reports
● Was able to build platform that could
be HIPAA compliant yet service dozens
of top healthcare companies
ABOUT: (company name private)
● Fortune 500 Healthcare Services
company
● Leading global provider of information,
innovative technology solutions and
contract research services helping
healthcare clients find better solutions
for patients.

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INNOVATION IN HEALTHCARE WITH ML
Personas Use Cases Engines Cloud
Data Engineering
Data Science
Data Analysts
Robotics
Sensor analysis
New product R&D
Spark
Hive
TensorFlow
AirFlow
AWSProduct &
Engineering Teams
Internal Systems
● Quickly deliver a data lake operation in
parallel with developing new robotics service
● Saw immediate cost savings on existing cloud
investments, which allowed the company to
focus on new product lines
● Able to build maintenance schedules for the
devices
● Data Science focusing on R&D use cases
OUTCOMES
Consumers

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How did they do it?
1
9
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Drug Discovery for Life Sciences
Deep Learning Example Distributed with Tensorflow

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How did they do it?
2
0
Copyright 2018 © Qubole
Machine Learning Introduction
• Input Data has known labels
(a.k.a. Training Data)
• Example Problems
• Classification
• Regression
• Example Algorithms
• Logistic Regression
• Decision Trees
Supervised
Learning
• Input Data has no labels
• Example Problems
• Clustering
• Dimensionality Reduction
• Example Algorithms
• K-Means
• Principal Component
Analysis (PCA)
Unsupervised
Learning
• Input data is a mixture of
labelled and unlabelled.
• Example Problems
• Classification
• Tracking & Navigation
• Example Algorithms
• Hidden Markov Model
• Kalman Filter
Semi-Supervised
Learning

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Deep Learning Introduction
Pedestrian Car Motorcycle Truck
+
1
+
1
+
1
Pedestrian
Car
Motorcycle
Truck
Bias
unit
+
1

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Data Science for Drug Discovery
● How accurately can we predict the efficacy and safety of new drugs?
● Predict molecular activity based on a molecule’s chemical structure.
Molecule Training Set
New Molecule
Machine Learning model Molecular Properties
https://www.kaggle.com/c/MerckActivity

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Technologies
Python-friendly open source library for numerical computation that
makes machine learning faster and easier. TensorFlow can train
and run deep neural networks & supports production prediction at
scale.
Keras is a high-level neural networks API, written in Python and
capable of running on top of TensorFlow, CNTK, or Theano.
Data processing engine.
Cloud-Native Data Platform for Self-Service AI, Machine Learning, and
Analytics.

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How did they do it?
2
4
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POLL QUESTION

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DEMO

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Sign up at www.qubole.com
Test Drive more examples on Qubole
www.Qubole.com

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STATE OF BIG DATA ADOPTION
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DATA SCIENCE REQUIRES SCALABLE BIG DATA
DATA CLOUD
50%savings in
cloud spend
1:65DataOps : Users
10Xincrease in
IoT data

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Modern ML Workflow is a Scientific Approach
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Healthcare Services Company
Personas Access Use Cases Engines Cloud
Data Engineering
Data Science
Machine Learning
Campaign Reports
Email analytics
Fraud detection
Spark
Hive
TensorFlow
AirFlow
AWSMarketing
Revenue
Management
Finance
Commercial teams
● Data Science teams are able scale their
products individually (rather than having one
shared multi-tenant environment)
● Saw immediate cost savings on existing cloud
investments, which allowed the company to
focus on R&D
● Able to go-to-market with new Data Science
products in 1-3 months
● Mitigate SLA delays on analytics reports
OUTCOMES