@article{98ee390f5fe64ed3b0f96396065e12cc,
title = "Using Machine Learning to Examine Suicidal Ideation After Traumatic Brain Injury",
abstract = "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.",
keywords = "Alcohol Use, Anxiety, Depression, Machine Learning, Suicidal Ideation, Traumatic Brain Injury",
author = "Fisher, {Lauren B.} and Curtiss, {Joshua E.} and Klyce, {Daniel W.} and Perrin, {Paul B.} and Juengst, {Shannon B.} and Gary, {Kelli W.} and Niemeier, {Janet P.} and Hammond, {Flora M.} and Bergquist, {Thomas F.} and Wagner, {Amy K.} and Rabinowitz, {Amanda R.} and Giacino, {Joseph T.} and Zafonte, {Ross D.}",
note = "Funding Information: LBF received salary support under award K23HD087464 from Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Funding Information: The contents of this publication were developed under grants from the National Institute on Disability, Independent Living, and Rehabilitation Research: Virginia Commonwealth University TBI Model System (#90DPTB0005); Spaulding/Harvard TBI Model System (#90DPTB0011); Indiana University TBI Model System (#90DRTB0002); North Texas TBI Model System (#90DPTB0013); Mayo Clinic TBI Model System (#90DPTB0012); Moss TBI Model System (#90DPTB0004); University of Washington TBI Model System(#90DPTB0008); and University of Alabama TBI Model System (#90DPTB0015); a Center within the Administration for Community Living (ACL), Department of Health and Human Services (HHS). The contents of this publication do not necessarily represent the opinions or views of the TBI Model Systems Centers, National Institute on Disability, Independent Living, and Rehabilitation Research, ACL, or HHS. Funding Information: The contents of this publication were developed under grants from the National Institute on Disability, Independent Living, and Rehabilitation Research: Virginia Commonwealth University TBI Model System (#90DPTB0005); Spaulding/Harvard TBI Model System (#90DPTB0011); Indiana University TBI Model System (#90DRTB0002); North Texas TBI Model System (#90DPTB0013); Mayo Clinic TBI Model System (#90DPTB0012); Moss TBI Model System (#90DPTB0004); University of Washington TBI Model System (#90DPTB0008); and University of Alabama TBI Model System (#90DPTB0015); a Center within the Administration for Community Living (ACL), Department of Health and Human Services (HHS). The contents of this publication do not necessarily represent the opinions or views of the TBI Model Systems Centers, National Institute on Disability, Independent Living, and Rehabilitation Research, ACL, or HHS. LBF received salary support under award K23HD087464 from Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Publisher Copyright: {\textcopyright} 2023 Lippincott Williams and Wilkins. All rights reserved.",
year = "2023",
month = feb,
day = "1",
doi = "10.1097/PHM.0000000000002054",
language = "English (US)",
volume = "102",
pages = "137--143",
journal = "American Journal of Physical Medicine",
issn = "0894-9115",
publisher = "Lippincott Williams and Wilkins",
number = "2",
}