Dr. Pestian's lab focuses on developing advanced technology for the care of neuropsychiatric illness. Using artificial intelligence, his team integrates analyses of trait and state characteristics for early identification of both neurological and psychiatric illness. His lab developed and implemented an automated, electronic health record surveillance system that processes clinician notes to identify epilepsy surgery candidates up to two years earlier than traditional approaches. The lab also focuses on earlier identification of individuals at risk of suicide, depression, and bipolar and anxiety disorders using verbal and non-verbal language.
Current projects include fusion of linguistic, acoustic, and visual cues that are being tested in selected Cincinnati Public Schools and Cincinnati Children’s clinics. Dr. Pestian and his lab have 18 issued patents and he is active in the entrepreneurial community. This activity has yielded over 500 jobs and one-half billion in revenue have been created. One invention, Processing Text With Domain-Specific Spreading Activation Methods, is a platform for neuropsychiatric research. Another, Optimization and Individualization of Medication Selection and Dosing, has been used for optimal mental health drug selection on more than 420,000 people. He and his colleagues have published more than 80 peer reviewed publications that focus on applied and translational sciences apropos to artificial intelligence.
He currently mentors five junior faculty, of which three have recently received funding from the National Institutes of Health (NIH). Dr. Pestian is an alumni of the NIH’s standing Study Section, Biomedical Library and Informatics Review Committee (BLIRC) of the National Library of Medicine, as well as the National Institute for Mental Health’s, Pathway to Independence (K99) study section.
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.
Nebulized Ipratropium Decreases Hospitalization Rate of Children with Severe Asthma • 389. Pediatric Research. 1998; 43:69.
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.
John P. Pestian, PhD, MBA, Michael T. Sorter, MD ...5/30/2023
John P. Pestian, PhD, MBA2/27/2023
John P. Pestian, PhD, MBA10/5/2022
John P. Pestian, PhD, MBA, Tracy A. Glauser, MD11/8/2021
John P. Pestian, PhD, MBA, Michael T. Sorter, MD ...2/10/2021
John P. Pestian, PhD, MBA8/5/2020