Abstract: Systems, apparatuses, methods, and computer program products are disclosed for automating incident severity classification. An example method includes: receiving a historical incident dataset; receiving a new incident dataset including information regarding a set of new incidents; generating a set of predicted severity classifications for the set of new incidents using a machine learning model, the historical incident dataset, and the new incident dataset; determining that a severity classification mismatch between at least one assigned severity classification and a predicted severity classification of the set of predicted severity classifications exists for the new incident dataset; and in response to determining that the severity classification mismatch exists, prompting further investigation of the severity classification mismatch through outputting an indication of the severity classification mismatch.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for automating incident severity classification. An example method includes: receiving a historical incident dataset; receiving a new incident dataset including information regarding a set of new incidents; generating a set of predicted severity classifications for the set of new incidents using a machine learning model, the historical incident dataset, and the new incident dataset; determining that a severity classification mismatch between at least one assigned severity classification and a predicted severity classification of the set of predicted severity classifications exists for the new incident dataset; and in response to determining that the severity classification mismatch exists, prompting further investigation of the severity classification mismatch through outputting an indication of the severity classification mismatch.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for automating incident severity classification. An example method includes receiving, by communications circuitry, a historical incident dataset, the historical incident dataset including information regarding a set of historical incidents and an assigned severity classification for each historical incident in the set of historical incidents. The example method further includes training, by a model generator and using the historical incident dataset, a machine learning model to classify incident severity. The example method further includes receiving, by the communications circuitry, a new incident dataset, generating, by a prediction engine and using the trained machine learning model and the new incident dataset, a set of predicted severity classifications for the set of new incidents, and outputting, by the communications circuitry, the predicted set of severity classifications for the set of new incidents.

Abstract: A roller for therapy with a temperature control to enable a user to combine the benefits of heat or cold with the use of a roller. The roller comprises a generally cylindrically shaped roller body, a heating element, fluid conducting elements, a power source, and a hollow core that houses components including the power source. The heating and cooling elements are electric and are configured within the housing or in an external component in different embodiments of the apparatus. Temperature control is available to the user through an interface configured on the housing or in an external component in different embodiments of the apparatus.