A Machine Learning Approach to Identifying the Thought Markers of Suicidal Subjects: A Prospective Multicenter Trial

Suicide and Life-Threatening Behavior

Volumn 47, Issue 1, 2017, Pages 112-121

  Pestian, John P ^a   Sorter, Michael ^a   Connolly, Brian ^a   Bretonnel Cohen, Kevin ^b   McCullumsmith, Cheryl ^c   Gee, Jeffry T ^d   Morency, Louis Philippe ^e   Scherer, Stefan ^f   Rohlfs, Lesley ^f   Faist, Robert ^g   Korbee, Leslie ^g   McCord, Lisa ^g   McCourt, Rachel ^g   Murphy, Colleen ^g  

^a UNIVERSITY OF CINCINNATI   (United States)

^b UNIVERSITY OF COLORADO SCHOOL OF MEDICINE   (United States)

^c UNIVERSITY OF CINCINNATI COLLEGE OF MEDICINE   (United States)

^d Princeton Community Hospital   (United States)

^e University of Southern California ^*  (United States)

^f University of Southern California   (United States)

^g NONE

Author keywords

[No Author keywords available]

Indexed keywords

ADOLESCENT; ADULT; ARTIFICIAL INTELLIGENCE; CLINICAL TRIAL; COMPUTER ASSISTED DIAGNOSIS; FEMALE; HUMAN; MACHINE LEARNING; MALE; MULTICENTER STUDY; PROCEDURES; PROGNOSIS; PROSPECTIVE STUDY; PSYCHOLOGY; SUICIDAL IDEATION; SUICIDE;

ADOLESCENT; ADULT; ARTIFICIAL INTELLIGENCE; DIAGNOSIS, COMPUTER-ASSISTED; FEMALE; HUMANS; MACHINE LEARNING; MALE; PROGNOSIS; PROSPECTIVE STUDIES; SUICIDAL IDEATION; SUICIDE;

EID: 85011841063     PISSN: 03630234     EISSN: 1943278X     Source Type: Journal    
DOI: 10.1111/sltb.12312     Document Type: Article

References (56)

1. Alku, P., Bäckström, T., & Vilkman, E. (2002). Normalized amplitude quotient for parametrization of the glottal flow. The Journal of the Acoustical Society of America, 112, 701–710.
2. Alku, P., Strik, H., & Vilkman, E. (1997). Parabolic spectral parameter—A new method for quantification of the glottal flow. Speech Communication, 22, 67–79.
3. American Psychiatric Association. (2003). Practice guideline for the assessment and treatment of patients with suicidal behaviors. American Journal of Psychiatry, 160(Suppl), 1–60.
4. Beck, A. T., Beck, R., & Kovacs, M. (1975). Classification of suicidal behaviors: I. Quantifying intent and medical lethality. The American Journal of Psychiatry, 132, 285–87.
5. Beck, A. T., Kovacs, M., & Weissman, A. (1979). Assessment of suicidal intention: The Scale for Suicide Ideation. Journal of Consulting and Clinical Psychology, 47, 343.
6. Bürk, F., Kurz, A., & Möller, H.-J. (1985). Suicide risk scales: Do they help to predict suicidal behaviour? European Archives of Psychiatry and Neurological Sciences, 235, 153–157.
7. Childers, D. G., & Lee, C. (1991). Vocal quality factors: Analysis, synthesis, and perception. The Journal of the Acoustical Society of America, 90, 2394–2410.
8. Columbia-Suicide Severity Rating Scale. (2015) Training–Public health settings. Retrieved from http://www.cssrs.columbia.edu/training_cssrs.html
9. Costanza, A., D'Orta, I., Perroud, N., Burkhardt, S., Malafosse, A., Mangin, P., et al. (2014). Neurobiology of suicide: Do biomarkers exist? International Journal of Legal Medicine, 128, 73–82.
10. Cummins, N., Scherer, S., Krajewski, J., Schnieder, S., Epps, J., & Quatieri, T. F. (2015). A review of depression and suicide risk assessment using speech analysis. Speech Communication, 71, 10–49.
11. D'Alessandro, C., & Sturmel, N. (2011). Glottal closure instant and voice source analysis using time-scale lines of maximum amplitude. Sadhana, 36, 601–622.
12. Degottex, G., Kane, J., Drugman, T., Raitio, T., & Scherer, S. (2014). Covarep—A collaborative voice analysis repository for speech technologies. In Proceedings of the International Conference on Acoustics, Speech and Signal Processing (pp. 960–964), Florence, Italy.
13. Desmet, B. (2014, May). Finding the online cry for help, PhD diss., Universiteit Gent, Gent, Belgium.
14. Dodge, Y. (2006). The Oxford dictionary of statistical terms: Entry for normalization. New York: Oxford University Press.
15. Drugman, T., & Alwan, A. (2011). Joint robust voicing detection and pitch estimation based on residual harmonics. In Interspeech (pp. 1973–1976), Florence, Italy.
16. Efron, B., & Tibshirani, R. (1997). Improvements on cross-validation: The.632 + bootstrap method. Journal of the American Statistical Association, 92, 548–560.
17. Goldstein, R. B., Black, D. W., Nasrallah, A., & Winokur, G. (1991). The prediction of suicide: Sensitivity, specificity, and predictive value of a multivariate model applied to suicide among 1906 patients with affective disorders. Archives of General Psychiatry, 48, 418–422.
18. Gomez, J. M. (2014). Language technologies for suicide prevention in social media. In Workshop on Natural Language Processing in the 5th Information Systems Research Working Days, (pp. 21-17), Quito, Ecuador.
19. Hacki, T. (1989). Klassifizierung von glottisdysfunktionen mit hilfe der elektroglottographie. Folia Phoniatrica et Logopaedica, 41, 43–48.
20. Hillenbrand, J., Cleveland, R. A., & Erickson, R. L. (1994). Acoustic correlates of breathy vocal quality. Journal of Speech, Language, and Hearing Research, 37, 769–778.
21. Huang, Y. P., Goh, T., & Liew, C. L. (2007). Hunting suicide notes in web 2.0-preliminary findings. In Proceedings of the 9th IEEE International Symposium on Multimedia (pp. 517–521). Taichung, Tiawan.
22. Hughes, D. H. (1995). Can the clinician predict suicide? Psychiatric Services, 46, 449–451.
23. Jashinsky, J., Burton, S. H., Hanson, C. L., West, J., Giraud-Carrier, C., Barnes, M. D., et al. (2015). Tracking suicide risk factors through twitter in the US. Crisis, 35, 51–59.
24. Joachims, T. (1998). Text categorization with support vector machines: Learning with many relevant features. Berlin: Springer.
25. Kane, J. (2012). Tools for analysing the voice. PhD diss., Trinity College, Dublin, Ireland.
26. Kovacs, M., & Garrison, B. (1985). Hopelessness and eventual suicide: A 10-year prospective study of patients hospitalized with suicidal ideation. American Journal of Psychiatry, 142, 559–563.
27. Kwon, O.-W., Chan, K., Hao, J., & Lee, T.-W. (2003). Emotion recognition by speech signals. In. Eurospeech (pp. 125–128), Geneva, Switzerland.
28. Large, M., & Ryan, C. (2014). Suicide risk assessment: Myth and reality. International Journal of Clinical Practice, 68, 679–681.
29. Le-Niculescu, H., Levey, D., Ayalew, M., Palmer, L., Gavrin, L., Jain, N., et al. (2013). Discovery and validation of blood biomarkers for suicidality. Molecular Psychiatry, 18, 1249–1264.
30. Li, T. M., Ng, B. C., Chau, M., Wong, P. W., & Yip, P. S. (2013). Collective intelligence for suicide surveillance in web forums. In Intelligence and security informatics (pp. 29–37). Berlin: Springer.
31. Matykiewicz, P., Duch, W., & Pestian, J. (2009). Clustering semantic spaces of suicide notes and newsgroups articles. In Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing (pp. 179–184). Stroudsburg, PA: Association for Computational Linguistics.
32. Molinaro, A., Simon, R., & Pfeiffer, R. (2005). Prediction error estimation: A comparison of resampling methods. Bioinformatics, 21, 3301–3307.
33. Müller, M. J., & Dragicevic, A. (2003). Standardized rater training for the Hamilton Depression Rating Scale (HAMD-17) in psychiatric novices. Journal of Affective Disorders, 77, 65–69.
34. Mundt, J. C., Greist, J. H., & Jefferson, J. W. (2013). Original research prediction of suicidal behavior in clinical research by. Journal of Clinical Psychiatry, 74, 887–893.
35. Newman, M. L., Pennebaker, J. W., Berry, D. S., & Richards, J. M. (2003). Lying words: Predicting deception from linguistic styles. Personality and Social Psychology Bulletin, 29, 665–675.
36. Olav Nielssen, M. (2012). Suicidal ideation and later suicide. The American Journal of Psychiatry, 169, 662.
37. Paris, J. (2006). Predicting and preventing suicide: Do we know enough to do either? Harvard Review of Psychiatry, 14, 233–240.
38. Pestian, J. P. (2010). A conversation with Edwin Shneidman. Suicide and Life-Threatening Behavior, 40, 516–523.
39. Pestian, J. P., Grupp-Phelan, J., Bretonnel Cohen, K., Meyers, G., Richey, L. A., Matykiewicz, P., et al. (2015). A controlled trial using natural language processing to examine the language of suicidal adolescents in the emergency department. Suicide and Life-Threatening Behavior, 46, 154–159.
40. Pestian, J. P., Matykiewicz, P., & Grupp-Phelan, J. (2008). Using natural language processing to classify suicide notes. In Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing (pp. 96–97). Stroudsburg, PA: Association for Computational Linguistics.
41. Pestian, J. P., Matykiewicz, P., & Linn-Gust, M. (2012). What's in a note: Construction of a suicide note corpus. Biomedical Informatics Insights, 5, 1.
42. Pokorny, A. D. (1983). Prediction of suicide in psychiatric patients: Report of a prospective study. Archives of General Psychiatry, 40, 249–257.
43. Posner, K., Brent, D., Lucas, C., Gould, M., Stanley, B., Brown, G., et al. (2008). Columbia-Suicide Severity Rating Scale (C-SSRS). New York, NY: New York State Psychiatric Institute.
44. Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. (2007). Section 16.5. Support vector machines. In Numerical recipes: The art of scientific computing, (3rd ed.). New York: Cambridge University Press.
45. Preston, E., & Hansen, L. (2005). A systematic review of suicide rating scales in shizophrenia. Crisis, 26, 170–180.
46. Scherer, S., Morency, L.-P., Gratch, J., Pestian, J., & Playa Vista, C. (2015). Reduced vowel space is a robust indicator of psychological distress: A crosscorpus analysis. In IEEE International Conference on Acoustics, Speech, and Signal Processing. (pp. 4789–4793), Brisbane, Australia.
47. Schölkopf, B., & Smola, A. (1998). Support vector machines. In Encyclopedia of biostatistics New York, NY: John Willey & Sons.
48. Sebastiani, F. (2002 March). Machine learning in automated text categorization. ACM Computing Surveys, 34, 1–47.
49. Thompson, P., Poulin, C., & Bryan, C. J. (2014). Predicting military and veteran suicide risk: Cultural aspects. In Proceedings from the Workshop on Computational Linguistics and Clinical Psychology (pp. 1–6) Baltimore, MD.
50. U.S. Food and Drug Administration. (2012). Guidance for industry: Suicidal ideation and behavior: Prospective assessment of occurrence in clinical trials. Silver Springs, MD: Author. Retrieved October, 5, 2012, from http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/htm.
51. Vapnik, V. N. (1999). An overview of statistical learning theory. IEEE Transactions on Neural Networks, 10, 988–999.
52. Venek, V., Scherer, S., Morency, L.-P., Rizzo, A., & Pestian, J. (2014). Adolescent suicidal risk assessment in clinician-patient interaction: A study of verbal and acoustic behaviors. In Proceedings of the IEEE Spoken Language Technology Workshop, (pp. 277–282), Lake Tahoe, Nevada.
53. Venek, V., Scherer, S., Morency, L.-P., Rizzo, A., & Pestian, J. (2016). Adolescent suicidal risk assessment in clinician-patient interaction. In IEEETransactions on Affective Computing.
54. Voorhees, E. M., Harman, D. K., et al. (2005). TREC: Experiment and evaluation in information retrieval. Cambridge: MIT Press.
55. Winters, N. C., Myers, K., & Proud, L. (2002). Ten-year review of rating scales. III: Scales assessing suicidality, cognitive style, and self-esteem. Journal of the American Academy of Child & Adolescent Psychiatry, 41, 1150–1181.
56. Zhang, L., Huang, X., Liu, T., Li, A., Chen, Z., & Zhu, T. (2015). Using linguistic features to estimate suicide probability of Chinese microblog users. In Human Centered Computing (pp. 549–559). Cham, Switzerland: Springer.