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

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.

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.

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.

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.