Abstract: The present disclosure describes a computational model of a learning network. The computational model may be implemented as instructions stored on a non-transitory memory that may be executed by a processor on a local machine or as part of a cloud-based architecture employing one or more multi-thread processors enabling different users to utilize the tool to enter data and visualize results simultaneously from different locations. The computational model may accept inputs corresponding to characteristics of a patient agent and characteristics of a clinician agent. The computational model may simulate how the patient agent and the clinician agent interact with respect to a treatment selection and efficacy and may additionally and iteratively simulate further interactions between the patient agent and the clinician agent. The computational model may record how the interactions between the patient agent and the clinician agent change patient agent outcomes over time.

Abstract: The multi-directional skates are a pair of roller skates, with each skate being an assembly having a skate boot, at least four roller assemblies, and a skid plate. The skate boot is a traditional in-line/aggressive skate-type boot including an upper shoe portion and a sole portion. The roller assemblies each include a substantially spherical roller or ball that act as rolling surfaces for skate, allowing it to create movement in any direction, not just forward and backward, but also sideways and complete 360 degree movement. The number and alignment of the roller assemblies may be modified according to the desires of the individual skater, but they are intended to be aligned such that each skate is capable of balancing itself it an upright position.