Abstract: Techniques are described for determining cognitive impairment, an example of which includes accessing a set of epochs of magnetoencephalography (MEG) data of responses of a brain of a test patient to a plurality of auditory stimulus events; processing the set of epochs to identify parameter values one or more of which is based on information from the individual epochs without averaging or otherwise collapsing the epoch data. The parameter values are input into a model that is trained based on the parameters to determine whether the test patient is cognitively impaired.

Abstract: Techniques are described for determining cognitive impairment, an example of which includes accessing a set of epochs of magnetoencephalography (MEG) data of responses of a brain of a test patient to a plurality of auditory stimulus events; processing the set of epochs to identify parameter values one or more of which is based on information from the individual epochs without averaging or otherwise collapsing the epoch data. The parameter values are input into a model that is trained based on the parameters to determine whether the test patient is cognitively impaired.

Abstract: Techniques are described for determining cognitive impairment, an example of which includes accessing a set of epochs of magnetoencephalography (MEG) data of responses of a brain of a test patient to a plurality of auditory stimulus events; processing the set of epochs to identify parameter values one or more of which is based on information from the individual epochs without averaging or otherwise collapsing the epoch data. The parameter values are input into a model that is trained based on the parameters to determine whether the test patient is cognitively impaired.

Abstract: Techniques are described for determining cognitive impairment, an example of which includes accessing a set of epochs of magnetoencephalography (MEG) data of responses of a brain of a test patient to a plurality of auditory stimulus events; processing the set of epochs to identify parameter values one or more of which is based on information from the individual epochs without averaging or otherwise collapsing the epoch data. The parameter values are input into a model that is trained based on the parameters to determine whether the test patient is cognitively impaired. Graphical user interfaces are described for presenting MEG epoch data and a score that correlates to a likelihood of the test individual being cognitively impaired.

Abstract: A critical data display process includes a computer directing a medical testing result for a patient to appear on a health care practitioner critical data display of a health care practitioner display monitor of a single health care practitioner. The process also includes the computer prompting the single health care practitioner through the health care practitioner critical data display to review the medical testing result for the patient on the health care practitioner critical data display, to designate or accept a previous designation of the medical testing result as either critical or noncritical, and to enter or authorize patient instructions. The process further includes the computer directing a patient critical data display to appear on a patient display monitor of a patient representative of the patient.

Abstract: A system may access electroencephalography (EEG) data of a subject, the EEG data including responses of the subject to an activation procedure. A system may analyze the EEG data including a plurality of epochs corresponding to the responses to identify first peaks, second peaks, and third peaks in one or more epochs. A system may determine values of a parameter in the plurality of epochs, the parameter being a characteristic of the first peak, the second peak, and/or the third peak. A system may generate a visual representation of the EEG data of the subject, the visual representation including an illustration of the first peak, the second peak and/or the third peak of a representative epoch or a heatmap compiled from the plurality of epochs, and the visual representation further including a graphical representation of the parameter that is presented as evidence of whether the subject is cognitively impaired.

Abstract: Techniques are described for determining cognitive impairment, an example of which includes accessing a set of epochs of magnetoencephalography (MEG) data of responses of a brain of a test patient to a plurality of auditory stimulus events; processing the set of epochs to identify parameter values one or more of which is based on information from the individual epochs without averaging or otherwise collapsing the epoch data. The parameter values are input into a model that is trained based on the parameters to determine whether the test patient is cognitively impaired.

Abstract: Techniques are described for determining cognitive impairment, an example of which includes accessing a set of epochs of magnetoencephalography (MEG) data of responses of a brain of a test patient to a plurality of auditory stimulus events; processing the set of epochs to identify parameter values one or more of which is based on information from the individual epochs without averaging or otherwise collapsing the epoch data. The parameter values are input into a model that is trained based on the parameters to determine whether the test patient is cognitively impaired.

Abstract: Techniques are described for determining cognitive impairment, an example of which includes accessing a set of epochs of magnetoencephalography (MEG) data of responses of a brain of a test patient to a plurality of auditory stimulus events; processing the set of epochs to identify parameter values one or more of which is based on information from the individual epochs without averaging or otherwise collapsing the epoch data. The parameter values are input into a model that is trained based on the parameters to determine whether the test patient is cognitively impaired.

Abstract: Techniques are described for determining cognitive impairment, an example of which includes accessing a set of epochs of magnetoencephalography (MEG) data of responses of a brain of a test patient to a plurality of auditory stimulus events; processing the set of epochs to identify parameter values one or more of which is based on information from the individual epochs without averaging or otherwise collapsing the epoch data. The parameter values are input into a model that is trained based on the parameters to determine whether the test patient is cognitively impaired. Graphical user interfaces are described for presenting MEG epoch data and a score that correlates to a likelihood of the test individual being cognitively impaired.

Abstract: Techniques are described for determining cognitive impairment, an example of which includes accessing a set of epochs of magnetoencephalography (MEG) data of responses of a brain of a test patient to a plurality of auditory stimulus events; processing the set of epochs to identify parameter values one or more of which is based on information from the individual epochs without averaging or otherwise collapsing the epoch data. The parameter values are input into a model that is trained based on the parameters to determine whether the test patient is cognitively impaired.

Abstract: A triage process includes a computer directing a user triaged data display to appear on a user display monitor of a designated user. The process also includes the computer prompting the designated user through the user triaged data display to initiate a triage task associated with a report appearing on the user triaged data display. The process further includes the computer prompting the designated user through the user triaged data display to assign the triage task to a triage partner. The process additionally includes the computer directing an alert message associated with the triage task to appear as part of a partner triaged data display on a partner display monitor of the triage partner based on inputs from the designated user. A triaged data display system includes a display monitor and a computer operatively connected to the display monitor. The computer is configured to implement a triage process.

Abstract: Techniques are described for determining cognitive impairment, an example of which includes accessing a set of epochs of magnetoencephalography (MEG) data of responses of a brain of a test patient to a plurality of auditory stimulus events; processing the set of epochs to identify parameter values one or more of which is based on information from the individual epochs without averaging or otherwise collapsing the epoch data. The parameter values are input into a model that is trained based on the parameters to determine whether the test patient is cognitively impaired.

Abstract: Techniques are described for determining cognitive impairment, an example of which includes accessing a set of epochs of magnetoencephalography (MEG) data of responses of a brain of a test patient to a plurality of auditory stimulus events; processing the set of epochs to identify parameter values one or more of which is based on information from the individual epochs without averaging or otherwise collapsing the epoch data. The parameter values are input into a model that is trained based on the parameters to determine whether the test patient is cognitively impaired.