Using pattern analysis matching to differentiate TBI and PTSD in a military sample.

Distinguishing between traumatic brain injury (TBI) residuals and the effects of posttraumatic stress disorder (PTSD) during neuropsychological evaluation can be difficult because of significant overlap of symptom presentation. Using a standardized battery of tests, an artificial neural network was used to create an algorithm to perform pattern analysis matching (PAM) functions that can be used to assist with diagnosis. PAM analyzes a patient's neuropsychological data and provides a best fit classification, according to one of four groups: TBI, PTSD, malingering/invalid data, or "other" (depressed/anxious/postconcussion syndrome/normal). The original PAM was modeled on civilian data; the current study was undertaken using a database of 100 active-duty army service personnel who were referred for neuropsychological assessment in a military TBI clinic. The PAM classifications showed 90% overall accuracy when compared with clinicians' diagnoses. The PAM function is able to classify detailed neuropsychological profiles from a military population with a high degree of accuracy and is able to distinguish between TBI, PTSD, malingering/invalid data, or "other." PAM is a useful tool to help with clinical decision-making.