Abstract: Systems, apparatuses and methods may provide technology that identifies minority class data and majority class data in patient-level data, wherein the minority class data corresponds to patients with a health failure and the majority class data corresponds to patients without the health failure, oversamples the minority class data to obtain synthetic class data and automatically reduces, via a machine learning classifier, a set of risk factor variables based on the majority class data, the minority class data and the synthetic class data.

Abstract: Systems, apparatuses and methods may provide technology that identifies minority class data and majority class data in patient-level data, wherein the minority class data corresponds to patients with a health failure and the majority class data corresponds to patients without the health failure, oversamples the minority class data to obtain synthetic class data and automatically reduces, via a machine learning classifier, a set of risk factor variables based on the majority class data, the minority class data and the synthetic class data.

Abstract: Techniques are provided to enable computing system backup, e.g., for use in disaster recovery. An environment includes a primary site and a secondary site each of which includes a network layer, a load-balancing layer, a web-server layer, an application layer, and a database layer. Generally, resources in both sites are concurrently active to provide services to customers, using the primary database layer in the primary site. Frequently, the data in this primary database layer is replicate to the secondary database layer in the secondary site. If the primary database layer is unserviceable, then the resources in both sites use the secondary database layer. If one or more resources in either site is unavailable, then the rest of the resources, in many situations, can still be available to provide services to the customers.