Researchers designed a machine learning algorithm able to predict risk of PTSD

The findings were published in Nature Medicine.

1 min read

New research in Nature Medicine has unveiled an algorithm using machine learning able to generate a post-traumatic stress disorder (PTSD) risk score from 70 clinical data points.

Machine learning is a form of artificial intelligence capable of recognizing patterns from data inputs and making predictions that otherwise would induce complications or impossibleness among human initiation.

“For many trauma patients, the ED visit is often their sole contact with the health care system. The time immediately after a traumatic injury is a critical window for identifying people at risk for PTSD and arranging appropriate follow-up treatment,” said Katharina Schultebraucks, co-author of the study.

“We selected measures that are routinely collected in the ED and logged in the electronic medical record, plus answers to a few short questions about the psychological stress response.”

“The idea was to create a tool that would be universally available and would add little burden to ED personnel.”

The algorithm was constructed using data from more than 350 adult trauma survivors in one U.S. city, followed by testing more than 200 patients in another major U.S. city.

The co-authors concluded that the “results demonstrate externally validated accuracy to discriminate PTSD risk with high precision.”

They also determined that “while the predictive algorithm yields useful reproducible results on two independent prospective cohorts of ED patients, future research should extend the generalizability to the broad, clinically heterogeneous ED population under conditions of routine medical care.”

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