Construction and validation of a predictive model for suicidal ideation in non-psychiatric elderly inpatients

Suicide poses a substantial public health challenge globally, with the elderly population being particularly vulnerable. Research into suicide risk factors among elderly inpatients with non-psychiatric disorders remains limited. This investigation focused on crafting a machine learning-based prediction model for suicidal ideation (SI) in this population to aid suicide prevention efforts in general hospitals.

A total of 807 non-psychiatric elderly inpatients aged over 60 were assessed using demographic and clinical data, and SI was measured using the Patient Health Questionnaire-9 (PHQ-9). Data were processed utilizing machine learning algorithms, and predictive models were developed using multiple logistic regression, Nomogram, and Random Forest models.

Key predictors included PHQ-8, Athens Insomnia Scale, hospitalization frequency, Perceived Social Support from Family scale, comorbidities, income, and employment status. Both models demonstrated excellent predictive performance, with AUC values exceeding 0.9 for both training and test sets. Notably, the Random Forest model outperformed others, achieving an AUC of 0.958, with high accuracy (0.952), precision (0.962), sensitivity (0.987), and an F1 score of 0.974.

These models offer valuable tools for suicide risk prediction in elderly non-psychiatric inpatients, supporting clinical prevention strategies.