1. poder.IO uses AI to predict customer behavior and personalize experiences. It deploys over 100 models daily using techniques like regression, classification, text analysis and deep learning. 2. Driverless AI is currently used to benchmark models before production and for research cases. It may be used starting Q3 2018 for advertising optimization, content classification, profile matching and look-alike modeling. 3. A joint team from poder.IO and Bayer developed models to predict individual medical test results using healthcare data, without direct lab measures. This could help improve treatment strategies. They used techniques like GLM, GBM, random forest and Driverless AI to develop and compare models for a medical test, finding Driver

1. Alexander Gedranovich
Chief Technology Officer
poder.IO
linkedin.com/in/alexander-gedranovich-73847435
Predicting medical
tests results using
Driverless AI

2. Outline
1. poder.IO Introduction
2. H2O at poder.IO
3. Cases for Driverless AI
4. Predicting medical tests results

3. poder.IO Introduction
Our main product is cloud platform EPICA.
EPICA uses AI to predict what your audience is going to do and when
with a high degree of accuracy.
You can use these predictions to have granular understanding of
customer journeys and personalize user’s experience at individual
level across web, email, social and digital advertising.

4. H2O at poder.IO
We update and deploy as API 100+ models daily (POJO / MOJO)
• Regression / Classification (GBM, GLM, RandomForest)
• Text Classification (Word2Vec +)
• Time Series Patterns (iSAX)
• Deep Networks (DeepWater + Tensorflow)
• Etc.

5. Cases for Driverless AI
At the moment:
1. Driverless AI as a benchmark for all models before production
2. Research Department for handle clients’ cases
Planning to use in production Q3 2018:
1. Advertising Campaigns Optimization
2. Content Classification
3. Profiles Matching
4. Look-a-like models

6. Predicting medical tests results
Disclaimer
The research was supported by Bayer AG.
The project was completed by the joint team of Data Scientists from
RocketScience.ai and Analytics from Bayer.
Currently RocketScience.ai team is a part of poder.IO.

7. Predicting medical tests results: Problem
The research goal is to develop an approach to predict individual
medical test results based on longitudinal medical and pharma claims
data without direct lab measures using data-driven analytic
techniques.
Such discoveries may result in improved treatment strategies.
TODO: // Substitute to graphics

8. Predicting medical tests results: Problem
• Medical laboratory test, which is required for making a decision on a
patient’s treatment strategy
• The test results are not available in most healthcare databases
• There is a need to predict the results of the test for any patient at any
point of time

9. Predicting medical tests results: Design

10. Predicting medical tests results: Design

11. Predicting medical tests results: Data
• 10 years time interval
• 11 M records
• 4 M unique patients
• Training data: 80%
• Test data: 20%
• Number of raw features: ~260

12. Predicting medical tests results: Prerequisites
Models / methods:
• ETL (C++, R, ggplot2)
• H2O.ai based GLM, GBM, Random Forest
• H2O.ai Driverless AI
Hardware:
• ETL, H2O models: 128Gb / 1Tb / 32 cores
• Driverless AI: AWS g3.8xlarge

13. Predicting medical tests results: Outcome
Model Training time RMSE R2 MAE Top features
GLM (ElasticNet) 00:13:20 16.477 0.5540 13.3785 100% original
GBM 100% original
Random Forest 100% original
Ensemble (3
models)
-
Ensemble (9
models)
-
Driverless AI 00:55:15 15.913 0.5857 12.8999 46% original
TODO: // Fill details

14. Predicting medical tests results: top 10 features
TODO: // Insert table with top 10 feature intersection from different
models

15. Predicting medical tests results: Surrogate model

16. Predicting medical tests results: Partial dependence

17. Thank you!