Abstract: Systems/techniques that facilitate automated training of machine learning classification for patient missed care opportunities or late arrivals are provided. In various embodiments, a system can access a set of annotated data candidates defined by two or more feature categories. In various aspects, the system can train a machine learning classifier on the set of annotated data candidates, thereby causing internal parameters of the machine learning classifier to become iteratively updated. In various instances, the system rank the two or more feature categories in order of classification importance, based on the iteratively updated internal parameters of the machine learning classifier. In various cases, the system can perform one or more electronic actions based on the two or more feature categories being ranked in order of classification importance.

Abstract: Systems, methods, and apparatus to generate and utilize predictive workflow analytics and inferencing are disclosed and described. An example apparatus includes memory circuitry including instructions and a plurality of artificial intelligence (AI) models; and processor circuitry to execute the instructions to implement at least: a smart scheduling engine to train at least one of the plurality of AI models, update at least one of the plurality of AI models, and inference a prediction using at least one of the plurality of AI models; and a smart scheduling application programming interface (API) to facilitate interaction with at least one of the plurality of AI models to trigger the prediction and to configure resources for an appointment based on the prediction.