Medical News Observer

A new study shows that machine learning is up to 93 percent accurate in correctly classifying a suicidal person and 85 percent accurate in identifying a person who is suicidal, has a mental illness but is not suicidal, or neither. These results provide strong evidence for using advanced technology as a decision-support tool to help clinicians and caregivers identify and prevent suicidal behavior, says John Pestian, PhD, professor in the divisions of Biomedical Informatics and Psychiatry at Cincinnati Children’s Hospital Medical Center and the study’s lead author. The study is published in the journal Suicide and Life-Threatening Behavior.

Dr. Pestian and his colleagues enrolled 379 patients in the study between Oct. 2013 and March 2015 from emergency departments and inpatient and outpatient centers at three sites. Those enrolled included patients who were suicidal, were diagnosed as mentally ill and not suicidal, or neither – serving as a control group.

Each patient completed standardized behavioral rating scales and participated in a semi-structured interview answering five open-ended questions to stimulate conversation, such as “Do you have hope?” “Are you angry?” and “Does it hurt emotionally?”

The researchers extracted and analyzed verbal and non-verbal language from the data. They then used machine learning algorithms to classify the patients into one of the three groups. The results showed that machine learning algorithms can tell the differences between the groups with up to 93 percent accuracy. The scientists also noticed that the control patients tended to laugh more during interviews, sigh less, and express less anger, less emotional pain and more hope.

Citation: Pestian, John P., Michael Sorter, Brian Connolly, Kevin Bretonnel Cohen, Cheryl McCullumsmith, Jeffry T. Gee, Louis‐Philippe Morency, Stefan Scherer, and Lesley Rohlfs. “A Machine Learning Approach to Identifying the Thought Markers of Suicidal Subjects: A Prospective Multicenter Trial.” Suicide and Life-Threatening Behavior (2016).
DOI: http://dx.doi.org/10.1111/sltb.12312
Adapted from press release by Cincinnati Children’s Hospital Medical Center