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Construction and Verification of a Risk Prediction Model for Suicidal Ideation in Patients With Bipolar Disorder: A

Xia Luo¹, Xiaoling Lin², Qinghua Zhao³

¹School of Nursing, Chongqing Medical University, 400016 Chongqing, China.

Alpha Psychiatry

|May 11, 2026

Summary

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Bipolar Disorder

Bipolar Disorder

Bipolar disorder is a chronic mental health condition marked by significant mood fluctuations, including episodes of mania and depression. Elevated energy levels, heightened mood or irritability, impulsive behavior, reduced sleep needs, rapid speech, racing thoughts, inflated self-esteem, and distractibility characterize mania. Individuals with bipolar disorder often alternate between depressive and manic states, with periods of emotional stability lasting an average of six months to a year.

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This study developed a random forest model to predict suicidal ideation in bipolar disorder patients. The model accurately identifies individuals at high risk, enabling timely clinical interventions.

Area of Science:

Psychiatry and Mental Health
Machine Learning in Healthcare
Clinical Psychology

Background:

Bipolar disorder (BD) is significantly linked to suicidal ideation (SI).
Effective prediction of SI in BD patients is crucial for early risk identification and intervention.
Developing robust predictive models can aid in managing high-risk populations.

Purpose of the Study:

To develop and validate a machine learning model for predicting suicidal ideation (SI) in patients with bipolar disorder (BD).
To identify key predictors of SI within the BD patient population.
To enhance early risk detection and inform targeted clinical interventions.

Main Methods:

A cross-sectional study involving 292 BD patients from three tertiary hospitals.

Keywords:

bipolar disorder machine learning random forest suicidal ideation

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A hybrid feature selection strategy combining the Boruta algorithm and clinical expertise.

Nine machine learning algorithms were evaluated, with the optimal model selected based on performance metrics like AUC and F1 score.

SHapley Additive exPlanations (SHAP) analysis was used for feature interpretation.

Main Results:

The random forest (RF) model achieved a high area under the curve (AUC) of 0.915, indicating strong predictive performance.
Key predictors of SI included quality of life, number of depressive episodes, history of suicide attempts, cognitive functioning, and childhood emotional abuse.
The model demonstrated a balanced accuracy of 0.818 and a sensitivity of 0.891.

Conclusions:

Random forest models are effective in predicting suicidal ideation in bipolar disorder patients.
These models can support the development of targeted interventions for individuals at risk.
Identifying key predictors like quality of life and past trauma can refine risk assessment strategies.