Abstract: Systems and method of the invention provide an in situ diagnostic system for traumatic brain injury (TBI). In implementations, a method includes: receiving, by a computing device, real-time user parameter data from one or more sensors of the user during a monitoring event; writing, by the computing device, the real-time user parameter data as time series data in a data store; determining, by the computing device, that at least one parameter of the real-time user parameter data meets or exceeds a predetermined parameter threshold value; calculating, by the computing device, a diagnostic score for the user based on the time series data, baseline parameter data of the user, and a determined protective equipment profile of the user; and automatically diagnosing a potential traumatic brain injury (TBI) of the user in situ based on the diagnostic score meeting or exceeding a diagnostic threshold.

Abstract: According to one embodiment, a method, computer system, and computer program product for diagnosing one or more blast injuries of an individual based on sustained blast exposure is provided. The present invention may include identifying the presence of a blast event, monitoring the location of an individual for the duration of the blast event; determining a blast exposure associated with the blast event for the individual; determining a sustained blast exposure of the individual comprising the blast exposures associated with a plurality of blast events during a single, continuous period of time; and, responsive to the sustained blast exposure of the individual exceeding one or more threshold values, prompting the individual to seek medical attention for one or more blast injuries associated with the one or more threshold values.

Abstract: The present disclosure provides for multi-stage treatment recommendations via analyzing a plurality of regimens for treatment of a condition based on a plurality of historical assessments of a plurality of patients who were treated using at least one of the plurality of regimens, wherein sets of historical assessments of the plurality of historical assessments track individual patients of the plurality of patients over time through changes in selected regimens of the plurality of regimens for the individual patients resulting in changes in levels of dysfunction; receiving a new assessment associated with a given patient; scoring, via a machine learning tool, the new assessment based on values for a plurality of variables included in the new assessment; and recommending a given regimen of the plurality of regimens for the given patient based on a category of the plurality of regimens that the plurality of ranked variables place the given patient into.