Neural network detection of files of suicidal patients and suicidal profiles

The optimal configuration of backpropagation (BP) neural networks was deter mined after 35 trials with different BP configurations evaluating the total detection rate. Ten different training and testing sets were used to ident ify optimal samples. All trials included sample files of patients with medi cally serious suicidal attempts (MSSA) and those of non-suicidal patients. Fifty files were used in each group for training and 49 files for testing w ith no overlap between the samples. The target variable for training was se riousness of suicide attempt (0 = non-suicidal, 1 = MSSA). The input set in cluded 44 demographic, clinical and patient-history variables. The optimal results showed that 93.8% of MSSA and 89.8% of the non-suicidal patient fil es were detected. Total success rate (TSR) was 91.8% and positive and negat ive prediction values (PPV, NPV) were 92% and 95.6%, respectively. Living a lone (6.76), treatment compliance (5.86), drug abuse or dependence (2.8), g lobal assessment of functioning (GAF) score (1.49), non-paranoid delusions (1.22) and suicide of first degree relative (1.1) were highly associated wi th MSSA according to the Garson calculation. However, logistic regression a ttributed high importance to hallucinations (p < 0.0001), diagnosis (p < 0. 002), number of children (p < 0.006), GAF score (p < 0.006), employment sta tus(p < 0.02) and stressors(p < 0.03). It was shown that: backpropagation n eural networks are very successful in identifying records of MSSA patients; a high GAF score is associated with high risk of MSSA and is the only comm on variable identified by both methods; and backpropagation identified two non-specific factors (living alone and treatment compliance) whereas statis tics found specific factors (hallucinations and diagnosis) highly associate d with MSSA.