Abstract: A method and system for forecasting demand for nursing services in a hospital herein. The method and system comprises accessing external data and historical data of a hospital. The method and system further comprises combining the external data and historical data of a hospital to form a structured data aggregation. Further, the structured data aggregation is processed. The method and system further comprises forecasting for a time interval, the demand for nursing services. In addition, work drivers are an accurate workload indicator for nurse workload planning and takes into consideration patient-dependent diversified needs. Furthermore, the system comprises a productivity index that accounts for nurses down-time and administrative tasks that need to be subtracted from the time spent on clinical tasks. This is used to schedule accurately the nurse rota based on realistic hospital needs and uses historic data to predict the upcoming demand over different ranges of time-horizons.

Abstract: An illustrative embodiment provides a computer-implemented method for processing wage garnishment court orders includes obtaining a court order for wage garnishment for a user. The method also includes predicting values for a first subset of a number of fields for executing wage garnishment according to the court order using a rule engine. The method also includes predicting values for a second subset of the number of fields using a machine learning engine, wherein the second subset of the number of fields includes missing fields for which the rule engine could not provide a prediction. The machine learning engine includes one of a plurality of classification algorithms. The one of the plurality of classification algorithms selected to use for the machine learning engine for predicting field values is selected according to at least one of accuracy, speed, size of model, and explainability.

Abstract: An illustrative embodiment provides a computer-implemented method for processing wage garnishment court orders includes obtaining a court order for wage garnishment for a user. The method also includes predicting values for a first subset of a number of fields for executing wage garnishment according to the court order using a rule engine. The method also includes predicting values for a second subset of the number of fields using a machine learning engine, wherein the second subset of the number of fields includes missing fields for which the rule engine could not provide a prediction. The machine learning engine includes one of a plurality of classification algorithms. The one of the plurality of classification algorithms selected to use for the machine learning engine for predicting field values is selected according to at least one of accuracy, speed, size of model, and explainability.