A Machine Learning Approach to Identifying the Thought Markers of Suicidal Subjects: A Prospective Multicenter Trial. Suicide and Life-Threatening Behavior. 2017; 47(1):112-121.
A Controlled Trial Using Natural Language Processing to Examine the Language of Suicidal Adolescents in the Emergency Department. Suicide and Life-Threatening Behavior. 2016; 46(2):154-159.
Methodological Issues in Predicting Pediatric Epilepsy Surgery Candidates Through Natural Language Processing and Machine Learning. Biomedical Informatics Insights. 2016; 8:11-18.
Assessing the similarity of surface linguistic features related to epilepsy across pediatric hospitals. Journal of the American Medical Informatics Association : JAMIA. 2014; 21(5):866-870.
Sentiment Analysis of Suicide Notes: A Shared Task. Biomedical Informatics Insights. 2012; 5(Suppl 1):3-16.
Nebulized Ipratropium Decreases Hospitalization Rate of Children with Severe Asthma • 389. Pediatric Research. 1998; 43(Suppl 4):69-69.
Comparison of Expert Vocabulary Usage Patterns Between Mental Health and Nonmental Health Clinicians When Diagnosing Pediatric Anxiety Disorders. Journal of Pediatrics. 2025; 286:114735.
Analyses of GWAS signal using GRIN identify additional genes contributing to suicidal behavior. Communications Biology. 2024; 7(1):1360.
Addressing the Pediatric Mental Health Crisis: Moving from a Reactive to a Proactive System of Care. Journal of Pediatrics. 2024; 265:113479.
Early Identification of Candidates for Epilepsy Surgery: A Multicenter, Machine Learning, Prospective Validation Study. Neurology. 2024; 102(4):e208048.
High dimensional predictions of suicide risk in 4.2 million US Veterans using ensemble transfer learning. Scientific Reports. 2024; 14(1):1793.
Developmental Epidemiology of Pediatric Anxiety Disorders. Child and Adolescent Psychiatric Clinics of North America. 2023; 32(3):511-530.
Automated, machine learning-based alerts increase epilepsy surgery referrals: A randomized controlled trial. Epilepsia. 2023; 64(7):1791-1799.
Identification of Novel, Replicable Genetic Risk Loci for Suicidal Thoughts and Behaviors Among US Military Veterans. JAMA psychiatry. 2023; 80(2):135-145.
Using iterative random forest to find geospatial environmental and Sociodemographic predictors of suicide attempts. Frontiers in Psychiatry. 2023; 14:1178633.
Implementation of Machine Learning Pipelines for Clinical Practice: Development and Validation Study. JMIR Medical Informatics. 2022; 10(12):e37833.
Toward Suicidal Ideation Detection with Lexical Network Features and Machine Learning. Northeast Journal of Complex Systems. 2022; 4(1).
Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors. Biological Psychiatry. 2022; 91(3):313-327.
Early identification of epilepsy surgery candidates: A multicenter, machine learning study. Acta Neurologica Scandinavica. 2021; 144(1):41-50.