Machine learning approach for the development of a crucial tool in suicide prevention: The Suicide Crisis Inventory-2 (SCI-2) Short Form.

The Suicide Crisis Syndrome (SCS) describes a suicidal mental state marked by entrapment, affective disturbance, loss of cognitive control, hyperarousal, and social withdrawal that has predictive capacity for near-term suicidal behavior. The Suicide Crisis Inventory-2 (SCI-2), a reliable clinical tool that assesses SCS, lacks a short form for use in clinical settings which we sought to address with statistical analysis. To address this need, a community sample of 10,357 participants responded to an anonymous survey after which predictive performance for suicidal ideation (SI) and SI with preparatory behavior (SI-P) was measured using logistic regression, random forest, and gradient boosting algorithms. Four-fold cross-validation was used to split the dataset in 1,000 iterations. We compared rankings to the SCI-Short Form to inform the short form of the SCI-2. Logistic regression performed best in every analysis. The SI results were used to build the SCI-2-Short Form (SCI-2-SF) utilizing the two top ranking items from each SCS criterion. SHAP analysis of the SCI-2 resulted in meaningful rankings of its items. The SCI-2-SF, derived from these rankings, will be tested for predictive validity and utility in future studies.

The role of suicide ideation in assessing near-term suicide risk: A machine learning approach.

BACKGROUND: The majority of suicide attempters do not disclose suicide ideation (SI) prior to making an attempt. When reported, SI is nevertheless associated with increased risk of suicide. This paper implemented machine learning (ML) approaches to assess the degree to which current and lifetime SI affect the predictive validity of the Suicide Crisis Syndrome (SCS), an acute condition indicative of imminent risk, for near-term suicidal behaviors (SB ). METHODS: In a sample of 591 high-risk inpatient participants, SCS and SI were respectively assessed using the Suicide Crisis Inventory (SCI) and the Columbia Suicide Severity Rating Scale (C-SSRS). Two ML predictive algorithms, Random Forest and XGBoost, were implemented and framed using optimism adjusted bootstrapping. Metrics collected included AUPRC, AUROC, classification accuracy, balanced accuracy, precision, recall, and brier score. AUROC metrics were compared by computing a z-score. RESULTS: The combination of current SI and SCI showed slightly higher predictive validity for near-term SB as evidenced by AUROC metrics than the SCI alone, but the difference was not significant (p<0.05). Current SI scored the highest amongst a chi square distribution in regards to predictors of near-term SB. CONCLUSION: The addition of SI to the SCS does not materially improve the model's predictive validity for near-term SB, suggesting that patient self-reported SI should not be a requirement for the diagnosis of SCS.

Assessing the predictive ability of the Suicide Crisis Inventory for near-term suicidal behavior using machine learning approaches.

OBJECTIVE: This study explores the prediction of near-term suicidal behavior using machine learning (ML) analyses of the Suicide Crisis Inventory (SCI), which measures the Suicide Crisis Syndrome, a presuicidal mental state. METHODS: SCI data were collected from high-risk psychiatric inpatients (N = 591) grouped based on their short-term suicidal behavior, that is, those who attempted suicide between intake and 1-month follow-up dates (N = 20) and those who did not (N = 571). Data were analyzed using three predictive algorithms (logistic regression, random forest, and gradient boosting) and three sampling approaches (split sample, Synthetic minority oversampling technique, and enhanced bootstrap). RESULTS: The enhanced bootstrap approach considerably outperformed the other sampling approaches, with random forest (98.0% precision; 33.9% recall; 71.0% Area under the precision-recall curve [AUPRC]; and 87.8% Area under the receiver operating characteristic [AUROC]) and gradient boosting (94.0% precision; 48.9% recall; 70.5% AUPRC; and 89.4% AUROC) algorithms performing best in predicting positive cases of near-term suicidal behavior using this dataset. CONCLUSIONS: ML can be useful in analyzing data from psychometric scales, such as the SCI, and for predicting near-term suicidal behavior. However, in cases such as the current analysis where the data are highly imbalanced, the optimal method of measuring performance must be carefully considered and selected.