YuLin Zhen, Photography Editor
A recent study by Yale researchers demonstrated the potential of a machine learning approach to predict symptoms of post-traumatic stress disorder, or PTSD, for recent trauma survivors.
Researchers have been studying the medical applications of machine learning for only around a decade, and the team focused their efforts on pushing the boundaries of this innovative tool with a unique experimental design. The research stands out as a crucial milestone, as their reported prediction strengths are relatively high for clinical measures.
“While a lot of studies usually are using cross-sectional designs and comparing patients with PTSD compared to healthy controls or compared to trauma-exposed healthy controls, this study focused on recent trauma survivors during the first 14 months after trauma exposure,” said Dr. Ziv Ben-Zion, a Fulbright postdoctoral fellow at Yale and first author of the study.
According to Ben-Zion, the data used in the study was “quite unique” and collected as part of his doctoral research from 2015 to 2020, at Tel Aviv Sourasky Medical Center in Israel.
Then, Ben-Zion recruited individuals who arrived at the emergency department after experiencing potentially traumatic events, the most common being car accidents.
The patients who experienced high levels of PTSD one month after admission — who were most likely to develop chronic PTSD — were assessed one month, six months and 14 months after admission. To monitor each patient’s progress, clinical assessments and fMRI scans, recording brain structure and function, were performed.
Ben-Zion shared good news: most of the patients recovered sometime during the 14 months of study.
By the end of data collection, Ben-Zion had obtained a multi-domain data set detailing PTSD symptom severity — CAPS-5 total scores, on a scale of zero to 80 — as well as cognitive functioning and neural data for each of the 171 participants.This data set was used to develop the predictive machine learning model. The team used connectome-based predictive modeling, a machine learning technique originally developed in the Constable Lab at Yale that has gained popularity over the past decade.The model works by applying 10-fold cross-validated regression models to whole-brain functional connectivity data derived from the fMRI BOLD […]
Yale researchers use machine learning to predict PTSD symptoms
