A Machine Learning Approach to Identifying the Thought Markers of Suicidal Subjects: A Prospective Multicenter Trial. Suicide and Life-Threatening Behavior. 2017; 47:112-121..
A Controlled Trial Using Natural Language Processing to Examine the Language of Suicidal Adolescents in the Emergency Department. Suicide and Life-Threatening Behavior. 2016; 46:154-159..
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. 2014; 21:866-870..
Sentiment Analysis of Suicide Notes: A Shared Task. Biomedical Informatics Insights. 2012; 5:3-16..
1998; 43:69.. Nebulized Ipratropium Decreases Hospitalization Rate of Children with Severe Asthma • 389. Pediatric Research.
Using iterative random forest to find geospatial environmental and Sociodemographic predictors of suicide attempts. Frontiers in Psychiatry. 2023; 14:1178633..
Developmental Epidemiology of Pediatric Anxiety Disorders. Child and Adolescent Psychiatric Clinics of North America. 2023; 32:511-530..
Automated, machine learning-based alerts increase epilepsy surgery referrals: A randomized controlled trial. Epilepsia. 2023; 64:1791-1799..
Identification of Novel, Replicable Genetic Risk Loci for Suicidal Thoughts and Behaviors Among US Military Veterans. JAMA Psychiatry. 2023; 80:135-145..
Implementation of Machine Learning Pipelines for Clinical Practice: Development and Validation Study. JMIR Medical Informatics. 2022; 10:e37833..
2022; 4.. Toward Suicidal Ideation Detection with Lexical Network Features and Machine Learning.
Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors. Biological Psychiatry. 2022; 91:313-327..
Cystic Fibrosis Point of Personalized Detection (CFPOPD): An Interactive Web Application. JMIR Medical Informatics. 2020; 8:e23530..
A Feasibility Study Using a Machine Learning Suicide Risk Prediction Model Based on Open-Ended Interview Language in Adolescent Therapy Sessions. International Journal of Environmental Research and Public Health. 2020; 17:E8187..
A Machine Learning Approach to Identifying Changes in Suicidal Language. Suicide and Life-Threatening Behavior. 2020; 50:939-947..
2020; 146:14-15.. Investigation of Racial Bias in an Epilepsy Machine Learning Algorithm Trained on Physician Notes. Pediatrics.
Identifying epilepsy psychiatric comorbidities with machine learning. Acta Neurologica Scandinavica. 2020; 141:388-396..
Dynamic predictive probabilities to monitor rapid cystic fibrosis disease progression. Statistics in Medicine. 2020; 39:740-756..
Investigation of bias in an epilepsy machine learning algorithm trained on physician notes. Epilepsia. 2019; 60:e93-e98..
2019; 144:37.. Prospective Evaluation of a Natural Language Processing System for Epilepsy Identification. Pediatrics.
(2019) What’s in a Word? Detecting Partisan Affiliation from Word Use in Congressional Speeches. Institute of Electrical and Electronics Engineers (IEEE). 00:1-8..
Implementation of Pharmacogenetics at Cincinnati Children's Hospital Medical Center: Lessons Learned Over 14 Years of Personalizing Medicine. Clinical Pharmacology and Therapeutics. 2019; 105:49-52..
Improving Detection of Rapid Cystic Fibrosis Disease Progression-Early Translation of a Predictive Algorithm Into a Point-of-Care Tool. IEEE Journal of Translational Engineering in Health and Medicine-JTEHM. 2019; 7:2800108..