This is my Insight health Data project

1. RISKPREDICTOR
Identify health risk
before it is too late
Dario Marrocchelli
Insight Health
Data Fellow

2. The problem
Wellness companies
do not know which
programs will be most
effective for a specific
population

3. My solution
RiskPredictor
A tool that identifies
those people at risk1
and their underlying
conditions
1 Risk is defined as an individual’s predicted healthcare cost

4. The data: unique, rich and messy
Unique de-identified data set from Zakipoint
that combines three types of information:
There are about 250,000 rows and 2,000 people
in these datasets

5. Cannot discuss
feature
engineering

6. Model performs very well
1 http://us.milliman.com/mara/
 Model performance in line with proprietary programs which cost $100,000+
 There is room for improvement (more data, more features, etc.)
R2Model
 RiskPredictor - Random Forest
 ACG1 (Commercial)
 RiskPredictor – Linear (Ridge)
 MARA1 (Commercial)
20.5%
29.7%
34.4%
57.9%

8. PhD in Chemistry
2006-2010
Postdoctoral
Associate
2010-2011
Postdoctoral
Fellow
2011-2013
Research
Scientist &
Instructor
2013-Present
Short Bio Fun Fact
I designed and taught at MIT a course
on the Science of Cooking (rated 9/10)
Computational Materials Scientist
working on renewable energy
30+ papers, 900+ citations

9. Extra slides

12. Model performs very well
1 http://us.milliman.com/mara/
There is room for improvement (more data,
more features, etc.)…
… but model performance is in line with
proprietary programs which cost $100,000+
R2Model
 RiskPredictor - Random Forest
 ACG1 (Commercial)
 RiskPredictor – Linear (Ridge)
 MARA1 (Commercial)
20.5%
29.7%
34.4%
57.9%

14. No diabetes
Diabetes (59)
(972)

15. No hypertension
Hypertension (210)
(821)

16. Data
Claims Biometric Behavioral

17. Communication with Zakipoint
Ramesh Kumar, CEO Heather Richie,
VP Product Management
 Several emails
 Google Hangout

18. Data
Unique data set from zph that combines:
1) Claim information (ICD-9 codes, medical costs, gender, age)
2) Biometric information (BMI, BP, cholesterol, A1C, etc.)
3) Behavioral (HRA and wellness program participation)
The dataset contains 2k lives and is in csv format (masked)
Obese
Overweight
Normal
Underweight (5)
(245)
(430)
(411)

19. Algorithm anatomy
Raw data
(icd-9 codes, BMI)
1000s diagnostic features
(e.g. diabetes, obesity, etc.)
Regression
(linear & random forest)
Predicted cost
(risk scores)