This is an introduction to the exciting world of healthcare data science though three real-world efforts we've delivered on in the past 3 years, and some of the open problems behind them. First, automated clinical coding will demonstrate the challenges in natural language understanding, variety of jargons and measurement stability. Second, patient risk prediction will illuminate the need for localized models, non-stationary models and explain-ability. Third, the master patient index covers some of the data quality, privacy and compliance constraints and the design choices they imply. We hope to convince that tackling these open problems is as intellectually exciting as it is socially important. Presented by David Talby, SVP Engineering at Atigeo.

1. #datapopupseattle
What Makes Healthcare Data
Science so Hard & Interesting
David Talby
SVP Engineering, Atigeo
davidtalby antigeo

2. #datapopupseattle
UNSTRUCTURED
Data Science POP-UP in Seattle
www.dominodatalab.com
D
Produced by Domino Data Lab
Domino’s enterprise data science platform is used
by leading analytical organizations to increase
productivity, enable collaboration, and publish
models into production faster.

3. David&Talby&
SVP&Engineering,&Atigeo&
@davidtalby
WHAT&MAKES&HEALTHCARE&DATA&SCIENCE
SO&HARD&&&INTERESTING

4. “TECHNOLOGY&WILL&REPLACE&80%&OF&WHAT&DOCTORS&DO”&
VINOD&KHOSLA,&2012
2 2

5. “as&many&as&40,500&patients&die&each&year&in&an&ICU&in&
the&U.S.&due&to&misdiagnosis”&Winters&et&al.,&2012,&John&Hopkins&&
“Combining&estimates&from&3&studies&yielded&a&rate&of&
outpatient&diagnostic&errors&of&5.08%,&or&12&million&US&
adults&every&year.”&Singhet&al.,&2014,&VA&Medical&Center
3
Root$cause:$being$human
Premature(closure Fatigue
Overconfidence Team&dynamics
Snap(judgment Prejudice

6. Real&time&monitoring
Always&up&to&date&with&science
Large&sample&size
Works&24x7&
No&trip,&no&waiting
Cheaper
More&accurate
More&objective
THE&PROMISE
4

7. 5
Big Challenge #1

8. “The&algorithm&was&able&
to&identify&the&fake&smiles&
92%&of&the&time.&
Humans,&on&the&other&
hand,&performed&no&better&
than&chance.”&
MIT,&2012
HUMAN&NUANCES
6
Can&you&distinguish&between&real&smiles&of&happiness&and&fake&smiles&
trying&to&mask&frustration?

9. “Algorithms&correctly&
predicted&which&atcrisk&
youth&would&go&on&to&
develop&psychosis&over&a&
2.5cyear&period&with&
100%&accuracy.”&
Bedi&et&al.,&Nature'Schizophrenia,'2015
MENTAL&HEALTH
7

10. SAMPLE&HYBRID&ANALYTICS&PIPELINE
8
Freectext&
clinical&notes
Relationships&
&&ontologies&
Sensors&&&
wearables
Graph&Features
Time&Series&
Features
NLP&Features
Direct&&&ambient&Feedback
Train&&&test&Classifiers
Imagery,&
drugs,&labs,&…
Train&&&test&ensembles

11. THE&OPEN&PROBLEM:&EXPLAINABILITY
9
@DavidJBianco,&http://www2.mlsecproject.org/blog/oncexplainabilitycincmachineclearning

12. 1 0
Big Challenge #2

13. Never&Changing Always&Changing
Online$Social$
Networking$Models/
Rules$
Banking$&$
eCommerce$fraud&
Cyber$Security
Automated$trading&
RealAtime$ad$bidding
Natural$Language,$
Social$Behavior$
Models
Political$&$
Economic$Models
Physical$models:&
Face$recognition&
Voice$recognition$
Climate$models
Google/Amazon&
Search$models
THE&MOMENT&YOU&PUT&A&MODEL&IN&PRODUCTION,&
IT&STARTS&DEGRADING
[Gunjan&Gupta,&Atigeo,&2014]

14. 100%&Offcline 100%&Online
Automated$ensemble,$
boosting$&$feature$
selection$techniques
Automated$
‘challenger’$online$
evaluation$&$
deployment
RealAtime$online$
learning$via$
passive$feedback
HandAcrafted$
machine$
learned$
models
Active$learning$via&
Active$feedback
Traditional
Scientific$Method:
Test$a$Hypothesis
Hard$Crafted$Rules
Daily/weekly$
batch$retraining
SO&PUT&THE&RIGHT&MACHINERY&IN&PLACE

15. 100%&Offcline 100%&Online
Automated$ensemble,$
boosting$&$feature$
selection$techniques
Automated$
‘challenger’$online$
evaluation$&$
deployment
RealAtime$online$
learning$via$
passive$feedback
HandAcrafted$
machine$
learned$
models
Active$learning$via&
Active$feedback
Traditional
Scientific$Method:
Test$a$Hypothesis
Hard$Crafted$Rules
Daily/weekly$
batch$retraining
STATE&OF&THE&PRACTICE&IN&HEALTHCARE

16. THE&OPEN&PROBLEM:&MODEL&EVALUATION
1 4
Evaluate&models&that&are:&
• Personalized&
• Localized&
• Evolve&over&time&
• Regulatory&acceptable&
?,'?

17. 1 5
Big Challenge #3

18. #datapopupseattle
@datapopup
#datapopupseattle

19. #datapopupseattle
Thank You To Our Sponsors

20. ‹ # › 16
@Atigeo&
@davidtalby

21. ©&2015&Atigeo,&Corporation.&All&rights&reserved.&&Atigeo&and&the&xPatterns&logo&are&trademarks&of&Atigeo.&The&information&herein&is&for&informational&purposes&only&and&represents&the&current&view&of&Atigeo&as&of&the&date&of&this&presentation.&&Because&Atigeo&must&
respond&to&changing&market&conditions,&it&should&not&be&interpreted&to&be&a&commitment&on&the&part&of&Atigeo,&and&Atigeo&cannot&guarantee&the&accuracy&of&any&information&provided&after&the&date&of&this&presentation.&&ATIGEO&MAKES&NO&WARRANTIES,&EXPRESS,&
IMPLIED&OR&STATUTORY,&AS&TO&THE&INFORMATION&IN&THIS&PRESENTATION.