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Neel Kishan, Technical Sales Leadneel@dato.com

Building Applications to Assess Patient Readmission Risk

Hello my name is

Neel KishanTechnical Sales Lead(former neuroscientist, GPU programmer, Eagle Scout, Chicago sports fan)

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neel@dato.comLet’s Schedule a Time to Talk: https://calendly.com/dato-neel

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

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

Dato products

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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

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

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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: neel@dato.com

Or Visit Us at the Data Science Summithttp://bit.ly/DSS-SF-2016

Discount Code: DSSFriend