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Article

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

by
Joshua Cohen
1,*,
Jennifer Wright-Berryman
2,
Lesley Rohlfs
1,
Donald Wright
1,
Marci Campbell
1,
Debbie Gingrich
3,
Daniel Santel
4 and
John Pestian
4
1
Clarigent Health, 5412 Courseview Drive, Suite 210, Mason, OH 45040, USA
2
Department of Social Work, College of Allied Health Sciences, University of Cincinnati, Cincinnati, OH 45221, USA
3
The Children’s Home, 5050 Madison Road, Cincinnati, OH 45227, USA
4
Department of Pediatrics, Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(21), 8187; https://doi.org/10.3390/ijerph17218187
Submission received: 30 September 2020 / Revised: 30 October 2020 / Accepted: 2 November 2020 / Published: 5 November 2020
(This article belongs to the Special Issue Novel Approaches to Suicide Prevention: Protective Factors and Clinical Interventions)
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Abstract

Background: 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. Method: Natural language processing machine learning models to identify level of suicide risk using a smartphone app were tested in outpatient therapy sessions. Data collection included language samples, depression and suicidality standardized scale scores, and therapist impression of the client’s mental state. Previously developed models were used to predict suicidal risk. Results: 267 interviews were collected from 60 students in eight schools by ten therapists, with 29 students indicating suicide or self-harm risk. During external validation, models were trained on suicidal speech samples collected from two separate studies. We found that support vector machines (AUC: 0.75; 95% CI: 0.69–0.81) and logistic regression (AUC: 0.76; 95% CI: 0.70–0.82) lead to good discriminative ability, with an extreme gradient boosting model performing the best (AUC: 0.78; 95% CI: 0.72–0.84). Conclusion: Voice collection technology and associated procedures can be integrated into mental health therapists’ workflow. Collected language samples could be classified with good discrimination using machine learning methods.
Keywords: machine learning; natural language processing; suicidal risk; risk assessment; mental health; therapy; suicidal ideation machine learning; natural language processing; suicidal risk; risk assessment; mental health; therapy; suicidal ideation

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MDPI and ACS Style

Cohen, J.; Wright-Berryman, J.; Rohlfs, L.; Wright, D.; Campbell, M.; Gingrich, D.; Santel, D.; Pestian, J. A Feasibility Study Using a Machine Learning Suicide Risk Prediction Model Based on Open-Ended Interview Language in Adolescent Therapy Sessions. Int. J. Environ. Res. Public Health 2020, 17, 8187. https://doi.org/10.3390/ijerph17218187

AMA Style

Cohen J, Wright-Berryman J, Rohlfs L, Wright D, Campbell M, Gingrich D, Santel D, Pestian J. A Feasibility Study Using a Machine Learning Suicide Risk Prediction Model Based on Open-Ended Interview Language in Adolescent Therapy Sessions. International Journal of Environmental Research and Public Health. 2020; 17(21):8187. https://doi.org/10.3390/ijerph17218187

Chicago/Turabian Style

Cohen, Joshua, Jennifer Wright-Berryman, Lesley Rohlfs, Donald Wright, Marci Campbell, Debbie Gingrich, Daniel Santel, and John Pestian. 2020. "A Feasibility Study Using a Machine Learning Suicide Risk Prediction Model Based on Open-Ended Interview Language in Adolescent Therapy Sessions" International Journal of Environmental Research and Public Health 17, no. 21: 8187. https://doi.org/10.3390/ijerph17218187

APA Style

Cohen, J., Wright-Berryman, J., Rohlfs, L., Wright, D., Campbell, M., Gingrich, D., Santel, D., & Pestian, J. (2020). A Feasibility Study Using a Machine Learning Suicide Risk Prediction Model Based on Open-Ended Interview Language in Adolescent Therapy Sessions. International Journal of Environmental Research and Public Health, 17(21), 8187. https://doi.org/10.3390/ijerph17218187

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