Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for explainable machine learning. In one aspect, a method comprises: obtaining a collection of data elements characterizing an entity; generating a plurality of features that collectively define a feature representation of the entity from the collection of data elements characterizing the entity; processing the feature representation of the entity using a machine learning model to generate a prediction for the entity; generating evidence data characterizing data elements from the collection of data elements that explain the prediction generated by the machine learning model for the entity; and providing an output comprising the prediction for the entity and the evidence data characterizing data elements from the collection of data elements that explain the prediction for the entity.

Abstract: A computerized system and method for estimating levels of obesity in an insured population using claims data. The model uses health risk assessment data comprising age, height, and weight information as well as information about health conditions and health behaviors for a member population. Claims data is used to train a two-stage model on the member population. The first stage comprises a support vector machine, a rule-based module, and a generalized linear model that estimates the probability of obesity. The second stage comprises a regression neural network that operates on the output of the first stage and a subset of the input feature vector. Cost and utilizations in these areas, along with overall health measures as well as demographics and social factors, are inputs to a set of pattern recognition engines that perform regression. The output is the estimated body mass index of the member.

Abstract: A computerized system and method for estimating levels of obesity in an insured population using claims data. The model uses health risk assessment data comprising age, height, and weight information as well as information about health conditions and health behaviors for a member population. Claims data is used to train a two-stage model on the member population. The first stage comprises a support vector machine, a rule-based module, and a generalized linear model that estimates the probability of obesity. The second stage comprises a regression neural network that operates on the output of the first stage and a subset of the input feature vector. Cost and utilizations in these areas, along with overall health measures as well as demographics and social factors, are inputs to a set of pattern recognition engines that perform regression. The output is the estimated body mass index of the member.