Using Machine Learning to Examine Suicidal Ideation After Traumatic Brain Injury

Lauren B. Fisher, Joshua E. Curtiss, Daniel W. Klyce, Paul B. Perrin, Shannon B. Juengst, Kelli W. Gary, Janet P. Niemeier, Flora M. Hammond, Thomas F. Bergquist, Amy K. Wagner, Amanda R. Rabinowitz, Joseph T. Giacino, Ross D. Zafonte

Research output: Contribution to journalArticlepeer-review


Objective: The aim of the study was to predict suicidal ideation 1 yr after moderate to severe traumatic brain injury. Design: This study used a cross-sectional design with data collected through the prospective, longitudinal Traumatic Brain Injury Model Systems network at hospitalization and 1 yr after injury. Participants who completed the Patient Health Questionnaire-9 suicide item at year 1 follow-up (N = 4328) were included. Results: A gradient boosting machine algorithm demonstrated the best performance in predicting suicidal ideation 1 yr after traumatic brain injury. Predictors were Patient Health Questionnaire-9 items (except suicidality), Generalized Anxiety Disorder-7 items, and a measure of heavy drinking. Results of the 10-fold cross-validation gradient boosting machine analysis indicated excellent classification performance with an area under the curve of 0.882. Sensitivity was 0.85 and specificity was 0.77. Accuracy was 0.78 (95% confidence interval, 0.77-0.79). Feature importance analyses revealed that depressed mood and guilt were the most important predictors of suicidal ideation, followed by anhedonia, concentration difficulties, and psychomotor disturbance. Conclusions: Overall, depression symptoms were most predictive of suicidal ideation. Despite the limited clinical impact of the present findings, machine learning has potential to improve prediction of suicidal behavior, leveraging electronic health record data, to identify individuals at greatest risk, thereby facilitating intervention and optimization of long-term outcomes after traumatic brain injury.

Original languageEnglish (US)
Pages (from-to)137-143
Number of pages7
JournalAmerican Journal of Physical Medicine and Rehabilitation
Issue number2
StatePublished - Feb 1 2023


  • Alcohol Use
  • Anxiety
  • Depression
  • Machine Learning
  • Suicidal Ideation
  • Traumatic Brain Injury

ASJC Scopus subject areas

  • Rehabilitation
  • Physical Therapy, Sports Therapy and Rehabilitation


Dive into the research topics of 'Using Machine Learning to Examine Suicidal Ideation After Traumatic Brain Injury'. Together they form a unique fingerprint.

Cite this