Webinar - Patient Readmission Risk
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Transcript of Webinar - Patient Readmission Risk
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11 Dato Confidential - Do not Distribute 1
Neel Kishan, Technical Sales [email protected]
Building Applications to Assess Patient Readmission Risk
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Hello my name is
Neel KishanTechnical Sales Lead(former neuroscientist, GPU programmer, Eagle Scout, Chicago sports fan)
2
[email protected]’s Schedule a Time to Talk: https://calendly.com/dato-neel
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Poll: Getting to know you
1. What do you do?2. Are you using a data-driven approach to reducing readmissions today?
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Why we are here today – Reducing Hospital ReadmissionsT
he
Pro
ble
m 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
Cu
rren
tSi
tuat
ion 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 N
ee
d f
or
Re
al-t
ime
Insi
gh
t 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
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Methods for Understanding Readmission Risk
Difficulty of Implementation
Pre
cis
ion
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
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Precise, Data Driven Healthcare Requires Machine Learning
• Data Quality Analysis
• Precision Medicine
• Radiology Image Analysis
• Fraud, Waste, and Abuse
• Connected Devices
• Clinical Decision Support
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Dato: The Platform for Real-Time Machine Learning
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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
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Dato products
9 Dato Confidential - Do not Distribute
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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 16Ru
nti
me
(s)
#MachinesCriteo (4B rows)
Logistic Regression
Common Crawl (100B rows)Netflix (100M rows)
Only platform with scalable Deep Learning, Boosted Trees, Graph Analytics, & more
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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
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Dato Products - The Agile Machine Learning Platform
Dato Confidential - Do not Distribute 12
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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?
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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
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Using Dato to Predict Early Readmission Based on 100,000 patient interactions
Demo
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Explanation
Advanced Readmission Risk Applications in Production
0100
Intercranial PressureLab ResultSaturation A
uto
matic
Feature
Extractio
n
Medical History, Labs,
Procedures
AutomaticFeatureExtraction
Risk Score
Advanced ML model
Provider Network Relationships
Intelligent Application
Patient-Provider Data
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Thank you!
Want to find out how to incorporate machine learning into your organization? Ping me
email: [email protected]
Or Visit Us at the Data Science Summithttp://bit.ly/DSS-SF-2016
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