Abstract: A case management tool uses a novel event forecast engine. The engine leverages a flexible and extensible data structure that combines diverse formats of claims (e.g., both medical and pharmacy) and that highlights “episodes” rather than items. The engine also makes predictions with respect to “cohorts” groups, where cohorts are defined using both static and dynamic features, the latter being features that change their values based on observation periods. Multiple definitions of cohorts are implemented, and optimal cohort definitions are estimated. The engine uses rolling time window processing to extract dynamic features in the data sets, and then one or more machine learning algorithms are applied to the extracted data. Cohort-wise machine learning models preferably learn on dynamic features, which are then put together for final predictions. A validation step is applied when outcomes are later observed. Validation results update the cohort definitions as well as the model parameters.

Abstract: An orthodontic appliance includes a latch that comprises at least one clip with a region for receiving an archwire. The clip also includes a recess for receiving a post of the appliance and for coupling the clip to remaining components of the appliance. The clip includes at least one protrusion that extends between the post-receiving recess and the archwire-receiving region, and the protrusion serves to retain the clip in place on the post.