Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-28T03:10:20.619Z Has data issue: false hasContentIssue false
  • English
  • Français

Validation of the Computerized Suicide Risk Scale – a backpropagation neural network instrument (CSRS-BP)

Published online by Cambridge University Press:  16 April 2020

I. Modai ,
M. Ritsner ,
R. Kurs ,
S. Mendel  and
A. Ponizovsky
I. Modai*
Affiliation:
Sha’ar Menashe Mental Health Center, Research Institute for Psychiatric Studies, Bruce Rappaport Faculty of Medicine, Technion, Haifa, Israel
M. Ritsner
Affiliation:
Sha’ar Menashe Mental Health Center, Research Institute for Psychiatric Studies, Bruce Rappaport Faculty of Medicine, Technion, Haifa, Israel
R. Kurs
Affiliation:
Sha’ar Menashe Mental Health Center, Research Institute for Psychiatric Studies, Bruce Rappaport Faculty of Medicine, Technion, Haifa, Israel
S. Mendel
Affiliation:
Rebecca Meirhoff Technical School, Tel Aviv University, Tel Aviv, Israel
A. Ponizovsky
Affiliation:
Rebecca Meirhoff Technical School, Tel Aviv University, Tel Aviv, Israel
*
*Correspondence and reprints. E-mail address: [email protected] (I. Modai).
Get access

Summary

Background

Medically serious suicide attempts have been recognized as the most important predictor of suicide. The Computerized Suicide Risk Scale based on backpropagation neural networks (CSRS-BP) has been recently found efficient in the detection of records of patients who performed medically serious suicide attempts (MSSA).

Objectives

To validate the CSRS-BP by: 1) using the CSRS-BP with patients instead of records; 2) comparing the ability of expert psychiatrists to detect MSSA, using the CSRS checklist; and 3) comparing the results of the Risk Estimator for Suicide (RES) and the self-rating Suicide Risk Scale (SRS) with the CSRS-BP.

Methods

Two hundred fifty psychiatric inpatients (35 MSSA and 215 non-MSSA) were diagnosed by clinicians using the SCID DSM-IV. Three expert psychiatrists completed the CSRS checklist, and the RES for each patient, and the patients completed the self-report SRS assessment scale. The CSRS-BP was run for each patient. Five other expert psychiatrists assessed the CSRS checklists and estimated the probability of MSSA for each patient. Comparisons of sensitivity and specificity rates between CSRS-BP, assessment scales and experts were done.

Results

Initially, the CSRS-BP, RES, SRS, and experts performed poorly. Although sensitivity and specificity rates significantly improved (two to four times) after the inclusion of information regarding the number of previous suicide attempts in the input data set, results still remained insignificant.

Conclusions

The CSRS-BP, which was very successful in the detection of MSSA patient records, failed to detect MSSA patients in face-to-face interviews. Information regarding previous suicide attempts is an important MSSA predictor, but remains insufficient for the detection of MSSA in individual patients. The detection rate of the SRS and RES scales was also poor and could therefore not identify MSSA patients or be used to validate the CSRS-BP.

Keywords

CSRSMSSA detectionRESSRS
Type
Original article
Information
European Psychiatry , Volume 17 , Issue 2 , April 2002 , pp. 75 - 81
Copyright
Copyright © Éditions scientifiques et médicales Elsevier SAS. 2002

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 4th edition. Washington DC: APA; 1994.Google Scholar
Baxt, GW. Application of artificial neural networks to clinical medicine. Lancet 1995 ; 346 : 1135–8.CrossRefGoogle ScholarPubMed
Beck, ATResnik, LPLettieri, DJ Eds. The prediction of suicide. Philadelphia: Charles Press; 1974.Google Scholar
Boone, JMSigillito, VGShaber, GS. Neural network in radiology: an introduction and evaluation in a signal detection task. Med Phys 1990 ; 17 : 234–41.CrossRefGoogle Scholar
Cullberg, JWasserman, DStefensson, CG. Who commits suicide after a suicide attempt? An 8 to 10 year follow up in a suburban catchment area. Acta Psychiatr Scand 1988 ; 77 : 598–603.CrossRefGoogle Scholar
Elliott, AJPages, KPRusso, JWilson, LGRoy-Byrne, PP. A profile of medically serious suicide attempts. J Clin Psychiatry 1996 ; 57: 567–71.CrossRefGoogle ScholarPubMed
First, MBSpitzer, RLGibbon, MWilliams, JBW. Structured Clinical Interview for DSM-IV Axis I Disorders, Patient Edition (SCID-P), version 2: New York: Stat Psychiatric Institute, Biometrics Research; 1995.Google Scholar
Garson, GD. Interpreting neural network connecting weights. AI Expert April 1991 : 47–51.Google Scholar
Goldstein, RBBlack, DWNasrallah, AWinokur, G. The prediction of suicide, sensitivity, specificity, and predictive value of a multivariate model applied to suicide among 1906 patients with affective disorders. Arch Gen Psychiatry 1991 ; 48 : 418–22.CrossRefGoogle ScholarPubMed
Hintze, JL. NCSS 6.0. Statistical System for Windows. User’s guide. Number Cruncher Statistical Systems: Kaysville, Utah; 1998.Google Scholar
Isometska, ELLonnqvist, JK. Suicide attempts preceding completed suicide. Br J Psychiatry 1998 ; 173 : 531–5.CrossRefGoogle Scholar
Kaplan, RDKottler, DBFrances, AJ. Reliability and rationality in the prediction of suicide. Hosp Community Psychiatry 1982 ; 33 : 212–5.Google ScholarPubMed
Ivarsson, TLarsson, BGillberg, C. A 2–4 year follow up of depressive symptoms, suicidal ideation, and suicide attempts among adolescent psychiatric inpatients. Eur Child Adolesc Psychiatry 1998 ; 7 : 96–104.CrossRefGoogle ScholarPubMed
Kay, SROpler, LALindenmayer, JP. Reliability and validity of the positive and negative syndrome scale for schizophrenia. Psychiatry Res 1988 ; 23 : 99–110.CrossRefGoogle Scholar
Krupinski, MFischer, AGrohmann, REngel, RHollweg, MMoller, HJ. Risk factors for suicides of inpatients with depressive psychoses. Eur Arch Psychiatry Clin Neurosci 1998 ; 248 : 141–7.CrossRefGoogle ScholarPubMed
Leenaars, AA. Clinical evaluation of suicide. Psychiatry Clin Neurosci 1995 ; 49 (Suppl 1) : s61–8.CrossRefGoogle ScholarPubMed
Leon, ACFriedman, RASweeney, JABrown, RPMann, JJ. Statistical issues in the identification of risk factors for suicidal behavior: the application of survival analysis. Psychiatry Res 1990 ; 31 : 99–108.CrossRefGoogle ScholarPubMed
Lin, CJ. A fuzzy adaptive learning control network with on-line structure and parameter learning. Int J Neural Syst 1996 ; 7 : 569–90.CrossRefGoogle ScholarPubMed
Litman, RE. Predicting and preventing hospital and clinic suicides. In: Maris, RWBerman, ALMaltsberger, JTYufit, RI Eds. Assessment and prediction of suicide. New York: Guilford Press; 1992. p. 448–66.Google Scholar
Maltsberger, JT. The psychodynamic formulation: aAn aid in assessing suicide risk. In: Maris, RWBerman, ALMalts-berger, JTYufit, RI Eds. Assessment and prediction of suicide. New York: Guilford Press; 1992. p. 25–49.Google Scholar
Mann, NHBrown, MD. Artificial intelligence in the diagnosis of low back pain. Orthop Clin N Am 1991 ; 22 : 303–14.Google Scholar
Maris, RWBerman, ALMaltsberger, JT. Summary and conclusions: what have we learned about suicide assessment and prediction? In: Maris, RWBerman, ALMaltsberger, JTYufit, RI Eds. Assessment and prediction of suicide. New York: Guilford Press; 1992. p. 640–53.Google Scholar
Modai, IValevski, ASolomish, AKurs, RHines, ILRitsner, M et al. Neural network detection of files of suicidal patients and suicidal profiles. Med0 Inform Internet Med 1999 ; 24 : 249–56.Google ScholarPubMed
Motto, JA. An integrated approach to estimating suicide risk. In: Maris, RWBerman, ALMaltsberger, JTYufit, RI Eds. Assessment and prediction of suicide. New York: Guilford Press; 1992. p. 625–39.Google Scholar
Motto, JAHeilborn, DCJuster, RP. Development of a clinical instrument to estimate suicide risk. Am J Psychiatry 1985 ; 14 : 680–6.Google Scholar
Plotchik, RVan Praag, HMConte, HRPicard, S. Correlates of suicide and violence risk 1: the suicide risk measure. Compr Psychiatry 1989 ; 30 : 296–302.CrossRefGoogle Scholar
Ritsner, MRabinowitz, JSlyuzberg, M. The Talbieh Brief Distress Inventory: a brief instrument to measure psychological distress among immigrants. Compr Psychiatry 1995 ; 36 : 448–53.CrossRefGoogle ScholarPubMed
Rosen, DH. The serious suicide attempt. Five-year follow-up study of 886 patients. JAMA 1976 ; 235 : 2105–9.CrossRefGoogle ScholarPubMed
Stephens, JHPascal, RMcHugh, PR. Suicide in patients hospitalized for schizophrenia: 1913–1940. J Nerv Ment Dis 1999 ; 8 : 671–7.Google Scholar
Tuckman, JYoungman, WF. A scale for assessing suicide risk of attempted suicides. J Clin Psychol 1968 ; 24 : 17–9.3.0.CO;2-T>CrossRefGoogle ScholarPubMed
Wasserman, PD. Neural computing theory and practice. New York: Wan Nostard Reinhold, ANZA Research Inclinations; 1989.Google Scholar
Zimet, GDDahlem, NWZimet, SGFarley, GK. The multidimensional scale of perceived social support. J Pers Assess 1988 ; 52 : 30–41.CrossRefGoogle Scholar
Zung, W. Index of potential suicide. In: Beck, ATResnik, LPLettieri, DJ Eds. The Prediction of Suicide. Philadelphia: Charles Press:; 1974.CrossRefGoogle Scholar
Submit a response

Comments

No Comments have been published for this article.