Scientific Publications

Peer reviewed analysis and research

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

A Feasibility Study Using a Machine Learning Suicide Risk Prediction Model Based on Open-Ended Interview Language in Adolescent Therapy Sessions
A Machine Learning Approach to Identifying Changes in Suicidal Language

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

A Machine Learning Approach to Identifying the Thought Markers of Suicidal Subjects: A Prospective Multicenter Trial
A Controlled Trial Using Natural Language Processing to Examine the Language of Suicidal Adolescents in the Emergency Department

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

Suicide Note Classification Using Natural Language Processing: A Content Analysis