John Pestian1, Daniel Santel1, Michael Sorter2, Ulya Bayram1,3, Brian Connolly1, Tracy Glauser4, Melissa DelBello5, Suzanne Tamang6, Kevin Cohen7. 1. Department of Pediatrics, Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. 2. Department of Pediatrics, Division of Psychiatry, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. 3. Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, OH, USA. 4. Department of Pediatrics, Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. 5. Department of Psychiatry & Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, USA. 6. Department of Biomedical Data Science, Center for Population Health Sciences, Stanford University, Stanford, CA, USA. 7. Computational Bioscience Program, University of Colorado School of Medicine, Denver, CO, USA.
Abstract
OBJECTIVE: 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. METHOD: In a multi-center study, 253 subjects were enrolled into either suicidal or control cohorts. Their responses to standardized instruments and interviews were analyzed using machine learning algorithms. Subjects were re-interviewed approximately 30 days later, and their language was compared to the original language to determine the presence of suicidal ideation. RESULTS: The results show that language characteristics used to classify suicidality at the initial encounter are still present in the speech 30 days later (AUC = 89% (95% CI: 85-95%), p < .0001) and that algorithms trained on the second interviews could also identify the subjects that produced the first interviews (AUC = 85% (95% CI: 81-90%), p < .0001). CONCLUSIONS: This approach explores the stability of suicidal language. When using advanced computational methods, the results show that a patient's language is similar 30 days after first captured, while responses to standard measures change. This can be useful when developing methods that identify the data-based phenotype of a subject.
OBJECTIVE: 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. METHOD: In a multi-center study, 253 subjects were enrolled into either suicidal or control cohorts. Their responses to standardized instruments and interviews were analyzed using machine learning algorithms. Subjects were re-interviewed approximately 30 days later, and their language was compared to the original language to determine the presence of suicidal ideation. RESULTS: The results show that language characteristics used to classify suicidality at the initial encounter are still present in the speech 30 days later (AUC = 89% (95% CI: 85-95%), p < .0001) and that algorithms trained on the second interviews could also identify the subjects that produced the first interviews (AUC = 85% (95% CI: 81-90%), p < .0001). CONCLUSIONS: This approach explores the stability of suicidal language. When using advanced computational methods, the results show that a patient's language is similar 30 days after first captured, while responses to standard measures change. This can be useful when developing methods that identify the data-based phenotype of a subject.
Authors: Joshua Cohen; Jennifer Wright-Berryman; Lesley Rohlfs; Donald Wright; Marci Campbell; Debbie Gingrich; Daniel Santel; John Pestian Journal: Int J Environ Res Public Health Date: 2020-11-05 Impact factor: 3.390
Authors: Joshua Cohen; Jennifer Wright-Berryman; Lesley Rohlfs; Douglas Trocinski; LaMonica Daniel; Thomas W Klatt Journal: Front Digit Health Date: 2022-02-02