Scientific Publications
Peer reviewed analysis and research
Abstract: Emergency departments (ED) are an important intercept point for identifying suicide risk and connecting patients to care, however, more innovative, person-centered screening tools are needed. Natural language processing (NLP) -based machine learning (ML) techniques have shown promise to assess suicide risk, although whether NLP models perform well in differing geographic regions, at different time periods, or after large-scale events such as the COVID-19 pandemic is unknown.
Frontiers in Digital Health
Pestian, John, et al.
2/2/2022
Abstract: As adolescent suicide rates continue to rise, innovation in risk identification is warranted. Machine learning can identify suicidal individuals based on their language samples. This feasibility pilot was conducted to explore this technology’s use in adolescent therapy sessions and assess machine learning model performance.
International Journal of Environmental Research and Public Health
Cohen, Joshua, et al.
11/5/2020
Abstract: With early identification and intervention, many suicidal deaths are preventable. Tools that include machine learning methods have been able to identify suicidal language. This paper examines the persistence of this suicidal language up to 30 days after discharge from care.
Biomedical Informatics Insights
Pestian, John, et al.
8/5/2010
Abstract: In this novel prospective, multimodal, multicenter, mixed demographic study, we used machine learning to measure and fuse two classes of suicidal thought markers: verbal and nonverbal.
Suicide and Life-Threatening Behavior
Pestian, John, et al.
6/2/2020
Abstract: What adolescents say when they think about or attempt suicide influences the medical care they receive. Mental health professionals use teenagers' words, actions, and gestures to gain insight into their emotional state and to prescribe what they believe to be optimal care.
Suicide and Life-Threatening Behavior
Pestian, John, et al.
8/7/2015
Abstract: Suicide is the second leading cause of death among 25–34 year olds and the third leading cause of death among 15–25 year olds in the United States. This paper presents our second attempt to determine the role of computational algorithms in understanding a suicidal patient's thoughts, as represented by suicide notes.
Biomedical Informatics Insights
Pestian, John, et al.
8/4/2010
