presented by Neel Kishan

1. 11 Dato Confidential - Do not Distribute1
Neel Kishan, Technical Sales Lead
neel@dato.com
Building Applications to
Assess Patient
Readmission Risk

2. Hello my name is
Neel Kishan
Technical Sales Lead
(former neuroscientist, GPU programmer,
Eagle Scout, Chicago sports fan)
2
neel@dato.com
Let’s Schedule a Time to Talk:
https://calendly.com/dato-neel

3. Poll: Getting to know you
1. What do you do?
2. Are you using a data-driven approach to
reducing readmissions today?
3

4. Why we are here today – Reducing Hospital Readmissions
TheProblem
Patient care requires
innovative methods
to address the
complexity for
improving
outcomes
Readmission rates
exceed 17% and
most of these are
avoidable
Medicare spends
$17B for avoidable
readmissions
CurrentSituation
Hospitals have started
to use analytics such
as the LACE index to
decrease readmission
rates
The Readmission
Reduction Program
(HRRP) reduces
payments up to 3%
for hospitals with
excess readmissions
for specific diagnoses
NeedforReal-timeInsight
Most analytic tools
are not specific and
do not leverage the
wealth of data stored
in EMRs, including
text, numeric, and
image data.
Predictive risk scoring
need to be
explainable to all
healthcare
professionals
4

5. Methods for Understanding Readmission Risk
Difficulty of Implementation
Precision
Intuition
• Health care professionals are experts
who understand emergent
phenomena
• Like all humans, prone to blind spots
Analytic
Approach
• Rules based
approaches provide
recommendations
on data
• They do not provide
actionable insights
Machine
Learning
• Can learn from highly complex data
and self organize to understand risk
• Provides real-time feedback to
healthcare professionals
• Analyzes the efficacy of proactive
measures
6

6. Precise, Data Driven Healthcare Requires Machine Learning
• Data Quality Analysis
• Precision Medicine
• Radiology Image Analysis
• Fraud, Waste, and Abuse
• Connected Devices
• Clinical Decision Support
7

7. Dato: The Platform for Real-Time Machine
Learning
8

8. 8
Dato’s Machine Learning Core Tenets
• Maps business tasks to machine learning routines
• Eliminates bottlenecks to production
• Simplifies iteration & understanding
Create Value Fast
• Easily combine any variety of features & ML tasks with any data
• Platform components are open, reusable, & sharable
• Easily extend & integrate with other frameworks
Flexibility to Innovate
• Make ML safe & consumable for the enterprise
• Easily deploy, manage, and improve ML as intelligent micro-services
• Adapt to a changing world that drifts from your historical data
Intelligence in Production

9. Dato products
9 Dato Confidential - Do not Distribute

10. 10
Dato’s Deep ML Capabilities
Application Toolkits
• Auto-select the best algorithm
• Auto-feature engineering for task
• App-centric visualizations
Robust Enterprise-Grade Algorithms
• 50+ of best-practice & novel algorithms
• Robust to real-world data
181#secs#
266#secs#
544#secs#
#
Dato#(10node)#
Spark#(50Node)#
#
Vowpal#Wabbit#
Time#(s)#
Matrix factorization PageRank
0
2000
4000
0 4 8 12 16
Runtime(s)
#Machines
Criteo (4B rows)
Logistic Regression
Common Crawl (100B rows)Netflix (100M rows)
Only platform with scalable Deep Learning,
Boosted Trees, Graph Analytics, & more

11. Dato
Predictive Services
GraphLab Create/
Dato Distributed
Rapid model building
Deploy as microservice
Live serving,
monitoring,
& model management
Iterate
and improve
on your infrastructure:
How Dato Makes Data Science Agile for Organizations
Dato Confidential - Do not Distribute11

12. Dato Products - The Agile Machine Learning Platform
Dato Confidential - Do not Distribute12

13. Poll: Data Science at your workplace
1. Does your team have data scientists or
software developers?
2. Are you using Machine Learning in
production today?
13

14. Readmission Scoring: Machine Learning Process
Supervised Machine Learning workflow:
Historical
Data
• Split train/test
datasets
• Readmissions&
non-
readmissions
Train ML
Model
• Use the
medical history
of patients
• Use interaction
of patients
Deploy
• Predict
likelihood to
be readmitted
to hospital
14

15. Using Dato to Predict Early Readmission
Based on 100,000 patient interactions
Demo
15

16. Explanation
Advanced Readmission Risk Applications in Production
0
100
Intercranial Pressure
Lab Result
Saturation
Automatic
Feature
Extraction
Medical
History,
Labs,
Procedures
Automatic
Feature
Extraction
Risk
Score
Advanced ML model
Provider Network Relationships
Intelligent
Application
Patient-Provider Data
16

17. Thank you!
Want to find out how to incorporate machine
learning into your organization? Ping me
email: neel@dato.com
Or Visit Us at the Data Science Summit
http://bit.ly/DSS-SF-2016
Discount Code: DSSFriend