Description: Second article in a series on how to use predictive analytics to improve the development of long-term care projection assumptions.

Description: Third article in a series on how to use predictive analytics to improve the development of long-term care projection assumptions.

Description: This article describes methodologies for detection and deterrence of Medicare fraud. It also describes the validation of PRIDIT for fraud detection in Medicare.

Description: We highlight a new approach leveraging predictive analytics to identify high risk and rising risk opioid users for more effective screening and follow-up care.

Description: First article in a series that walks through the progression from developing LTC assumptions using traditional methods to doing so using predictive analytics.

Description: Even the best algorithms are only effective if they are trained on appropriate data. This article discusses common pitfalls in creating a training data set.

Description: The newer predictive modeling tools are powerful but they work best when supported by a lot of computing power. Parallel processing is very powerful but often very difficult to implement. Dihui and Richard take us through the maze of options with H2O, SparkNet/MLib and Mahout. Which do you use for a generalized linear model (GLM), and which is better for high-volume data manipulation?

Description: Evaluates the ability of various model types to automatically approximate various mathematical formulas. This can be used as a guide for feature engineering.

Description: This article describes a method that can be useful when re-fitting a risk score model to a population of only moderate size. Learn how to use concepts like ridge regression to train a model with partial credibility.