Classification of Adolescent Psychiatric Patients at High Risk of Suicide Using the Personality Assessment Inventory by Machine Learning

Abstract

Objective: There are growing interests on suicide risk screening in clinical settings and classifying high-risk groups of suicide with suicidal ideation is crucial for a more effective suicide preventive intervention. Previous statistical techniques were limited because they tried to predict suicide risk using a simple algorithm. Machine learning differs from the traditional statistical techniques in that it generates the most optimal algorithm from various predictors.

Methods: We aim to analyze the Personality Assessment Inventory (PAI) profiles of child and adolescent patients who received outpatient psychiatric care using machine learning techniques, such as logistic regression (LR), random forest (RF), artificial neural network (ANN), support vector machine (SVM), and extreme gradient boosting (XGB), to develop and validate a classification model for individuals with high suicide risk.

Results: We developed prediction models using seven relevant features calculated by Boruta algorithm and subsequently tested all models using the testing dataset. The area under the ROC curve of these models were above 0.9 and the RF model exhibited the best performance.

Conclusion: Suicide must be assessed based on multiple aspects, and although Personality Assessment Inventory for Adolescent assess an array of domains, further research is needed for predicting high suicide risk groups.

Keywords: Child and adolescent psychiatry; Machine learning; Personality Assessment Inventory-Adolescent; Suicide.

Figure 1.

Schematic of prediction model development. LR, logistic regression; RF, random forest; ANN, artificial neural network; SVM, support vector machine; XGB, extreme gradient boosting.

Figure 2.

Receiver operating characteristic curves plotted from machine learning models developed with sex, age, and Personality Assessment Inventory for Adolescent scales. AUROC, area under the ROC curve; CI, confidence interval; LR, logistic regression; RF, random forest; ANN, artificial neural network; SVM, support vector machine; XGB, extreme gradient boosting.

Figure 3.

Relative feature importance computed using the Boruta algorithm. The blue violin plots correspond to the minimal, average, and maximum Z scores of a shadow attribute. The red and green violin plots represent the Z scores of the rejected and confirmed attributes, respectively. The black dots and horizontal lines inside each violin plot represent the mean and median values, respectively. All features that received a lower relative feature importance than that of the shadow feature were defined as irrelevant for prediction. Laterality was considered an irrelevant feature (marked in red). ICN, inconsistency; INF, infrequency; NIM, negative impression; PIM, positive impression; SOM, somatic complaints; ANX, anxiety; ARD, anxiety-related disorders; DEP, depression; MAN, mania; PAR, paranoia; SCZ, schizophrenia; BOR, borderline features; ANT, antisocial fea-tures; ALC, alcohol problems; DRG, drug problems; AGG, aggression; STR, stress; NON, nonsupport; RXR, treatment rejection; DOM, dominance; WRM, warmth.

Figure 4.

Receiver operating characteristic curves plotted from machine learning models developed with selected features by Boruta algorithm. AUROC, area under the ROC curve; CI, confidence interval; LR, logistic regression; RF, random forest; ANN, artificial neural network; SVM, support vector machine; XGB, extreme gradient boosting.