Predicting High-Cost Health Insurance Members through Boosted Trees and Oversampling: An Application Using the HCCI Database
Author:
Affiliation:
1. Department of Statistics, Brigham Young University, Provo, Utah
2. HealthCare Analytical Solutions, Inc., Bend, Oregon
Funder
Society of Actuaries
Publisher
Informa UK Limited
Subject
Statistics, Probability and Uncertainty,Economics and Econometrics,Statistics and Probability
Link
https://www.tandfonline.com/doi/pdf/10.1080/10920277.2020.1754242
Reference22 articles.
1. XGBoost
2. Bayesian nonparametric predictive modeling of group health claims
3. Comparing Credibility Estimates of Health Insurance Claims Costs
4. Using Information on Clinical Conditions to Predict High-Cost Patients
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