Clarigent Health, in affiliation with major research institutions and nationally and internationally regarded clinician-scientists, conducts research in advanced technologies to assist in both the understanding of and recommendation of treatment for behavioral and mental health conditions. This research provides new methods to assist clinicians in diagnosing and treating mental illnesses such as major depression, anxiety disorders, and cognitive and memory problems in adolescents, adults, and senior individuals. We are particularly interested in research to assist in identification of individuals that are in danger of inward or outward crisis, including suicide and violence prevention.
Classification and Assessment of Mental Health Performance Using Schematics
In collaboration with Cincinnati Children's Hospital, The Children's Home of Cincinnati, and Talbert House, we are gathering 5,000 voiceprints to help in understanding mental health conditions in adolescents and young adults.
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Classification and Assessment of Mental Health Performance Using Schematics - Pilot
In this IRB approved study, conducted in collaboration with The Children’s Home of Cincinnati, multiple mental health interviews were conducted with students ages 8-21 at 8 schools utilizing 10 licensed mental health therapists.
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. Machine learning algorithms were used with the subjects’ words and vocal characteristics to classify 379 subjects recruited from two academic medical centers and a rural community hospital into one of three groups: suicidal, mentally ill but not suicidal, or controls. By combining linguistic and acoustic characteristics, subjects could be classified into one of the three groups with up to 85% accuracy. The results provide insight into how advanced technology can be used for suicide assessment and prevention.
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. This prescription is often inconsistent among caregivers, however, and leads to varying outcomes. This variation could be reduced by applying machine learning as an aid in clinical decision support. We designed a prospective clinical trial to test the hypothesis that machine learning methods can discriminate between the conversation of suicidal and nonsuicidal individuals. Using semisupervised machine learning methods, the conversations of 30 suicidal adolescents and 30 matched controls were recorded and analyzed. The results show that the machines accurately distinguished between suicidal and nonsuicidal teenagers.
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. In the Emergency Department, where suicidal patients often present, estimating the risk of repeated attempts is generally left to clinical judgment. 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. We focus on developing methods of natural language processing that distinguish between genuine and elicited suicide notes. We hypothesize that machine learning algorithms can categorize suicide notes as well as mental health professionals and psychiatric physician trainees do. The data used are comprised of suicide notes from 33 suicide completers and matched to 33 elicited notes from healthy control group members. Eleven mental health professionals and 31 psychiatric trainees were asked to decide if a note was genuine or elicited. Their decisions were compared to nine different machine-learning algorithms. The results indicate that trainees accurately classified notes 49% of the time, mental health professionals accurately classified notes 63% of the time, and the best machine learning algorithm accurately classified the notes 78% of the time. This is an important step in developing an evidence-based predictor of repeated suicide attempts because it shows that natural language processing can aid in distinguishing between classes of suicidal notes.
Clarigent Health funds studies through grants and in-kind contributions across multiple disciplines at all levels of investigation. We also seek to partner with companies and institutions in ongoing and novel research.