Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for improving allocation of limited resources by generating an optimization function for generating an optimal amount of resources to allocate to data objects in each of one or more data object cohorts based on nonlinear causal effect predictions and generating an optimal parameter occurrence set based on the determined optimal amount of resource. Nonlinear causal effects of selected amounts of type-varied resources assigned to specific data objects are predicted on an outcome of interest associated with the data objects.

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for improving allocation of limited resources by determining an optimal amount of resources to allocate to resource-receiving entities in each of one or more resource-receiving entity cohorts based on non-linear causal effect predictions, and determining an optimum operation configuration based on the determined optimal amount of resource. Non-linear causal effect of selected amounts of resources assigned to specific resource-receiving entities are predicted on an outcome of interest associated with the resource-receiving entities.

Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for allocating resources. The method comprises receiving, by a computing device using a resource allocation machine learning framework, historical data comprising one or more causal variables corresponding to one or more actions or inactions with respect to one or more resource-requesting entities and one or more outcomes of one or more actions, identifying, by the computing device, given ones of one or more resource-requesting entity subgroups based at least in part on the one or more causal effect predictions, and performing, by the computing device, one or more prediction-based actions based at least in part on the identification of the given one or more resource-requesting entity subgroups.

Abstract: There is a need for more effective and efficient health risk assessment and prevention. This need is addressed by utilizing microvascular information as a component in an integrated health risk score. An example method can include receiving a microvascular data object for a user that is indicative of a microvascular volume differential for the user; determining a plurality of predictive outcome features for a predicted microvascular outcome; determining, using a feature prediction model, one or more impact predictions for the plurality of predictive outcome features; generating, based at least in part on the microvascular data object and the one or more impact predictions, an outcome risk score data object for the user; and generating, using a refined remedial model, and based at least in part, on the outcome risk score data object, at least one tailored remedial measure for the user.