Abstract: Methods, systems, and apparatus, including computer programs encoded on a storage device, for dynamic population stratification based on changing entity attributes. In one aspect, a method includes actions of obtaining data from a data source that describes attributes associated with an entity, determining, based on the obtained data, a first ranking of the entity based on the severity state of the entity, determining, based on the obtained data, a second ranking of the entity based on the acuity state of the entity, adjusting the first ranking of the entity based on the second ranking of the entity, and assigning the entity to a particular risk category based on the adjusted ranking.

Abstract: Methods, systems, and apparatus for training model parameters stored in shared memory to predict risk. The method may include obtaining training data that includes a plurality of training data structures that each represent attributes of an entity, wherein each training data structure represents (i) features derived from a first set of categories defined by a first model and from a second set of categories defined by a second model, and (ii) a risk-level associated with the entity. For each respective training data structure, providing the training data structure as an input to the model, receiving an output from the model based on the model's processing of the training data structure, determining an amount of error between the output of the model and the risk-level of the training data structure, and adjusting a parameter value of the model stored in a shared memory based on the determined error.