“Using machine learning, researchers were able to cut six of the 20 questions used to diagnose post-traumatic stress disorder (PTSD) while still maintaining accuracy in a veteran population, according to a study published in Assessment.”
“PTSD impacts eight million adults in the US, the researchers stated, including hundreds of thousands of veterans of the conflicts in Iraq and Afghanistan. With the onset of the COVID-19 pandemic, PTSD symptoms are also increasing among the general population, the team noted.”
“However, diagnosing PTSD is time-consuming – the process typically takes 30 minutes or more, which is too long for most clinical visits.”
“To streamline PTSD diagnosis, researchers from the VA Boston Healthcare System and the Boston University School of Public Health (BUSPH) set out to develop a machine learning tool that would make the process more efficient.”
“The team used data from the Structured Clinical Interview for the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (SCID-5) assessments of 1,265 veterans who served in Iraq and Afghanistan.”
“Researchers built a machine learning model that learned how strongly different terms in the diagnostic predicted PTSD diagnosis. This enabled the team to identify which items had weak associations and could be cut, while maintaining at least 90 percent accuracy…”
“Despite the promising results, the researchers emphasized that the machine learning algorithm won’t replace human providers, but could act as a companion tool for more efficient PTSD diagnosis…” Read the full article here.
Source: Machine Learning Can Streamline PTSD Diagnosis in Veterans – By Jessica Kent, October 1, 2020. HealthITAnalytics.
