I am David Raymond Kearney, political economist by training, data scientist by profession.
As a data scientist, I create automated member and provider engagement campaigns that leverage e-mail, IVR, SMS and live calls to increase medication adherence.
I also design experiments and employ randomized control trials to improve campaigns by identifying the most effective campaign variants, and productionlize machine learning models to identify members at risk of behaving in a way that contributes to poor health outcomes and most likely to benefit from campaigns.
I got my Ph.D. at Duke University, where my research focused on applying econometric and statistical techniques to explain and predict the allocation of fiscal transfers.
This site, will cover spark, h2o. hive and data science using python.
Posts
Forecasting running data
Modeling Health Care Data App
Modeling Health Care Data App
Xgboost for Health Data (Pipeline Step 3)
Modeling Health Care Data
EDA for Health Data (Pipeline Step 2)
Regression using Fiscal Data with PyCaret
CausalML Uplift Tree Visualization
CausalML uplift with tree-based algorithms
Imbalanced data
Model Inspection
Model Calibration
Model Evaluation
Preprocessing example in Sklearn
sklearn-pipelines-example
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