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Predicting Suicidal Ideation From Depression Screening Data: A Network-augmented Machine Learning Approach.

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Predicting Suicidal Ideation From Depression Screening Data: A Network-augmented Machine Learning Approach.

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Predicting suicidal ideation from depression screening data: A network-augmented machine learning approach.

Hanjoo Kim¹, Anastasia K Yocum¹, Melvin G McInnis¹

¹Department of Psychiatry, University of Michigan, MI, USA.

Journal of Affective Disorders

|March 25, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

Machine learning and network analysis improve suicidal ideation screening. The novel model enhances detection accuracy from routine depression symptom data, aiding early case finding.

Keywords:

Depression Machine learning National Health and Nutrition Examination Survey (NHANES)Network analysis Patient Health Questionnaire-9 (PHQ-9)Suicidal ideation

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

Computational psychiatry
Network science
Machine learning in healthcare

Background:

Suicidal ideation assessment often relies on single self-report items.
Routine depression symptom data can be leveraged for improved screening.
A novel model combining machine learning and network analytics was developed.

Purpose of the Study:

To infer an auxiliary signal for suicidal ideation from routine depressive symptom data.
To evaluate the performance of machine learning models augmented with network features.
To compare the novel model against traditional screening methods.

Main Methods:

Utilized data from the National Health and Nutrition Examination Survey (N=44,922).
Developed three predictive models: PHQ-8 total score, PHQ-8 items, and items plus network features (centrality, edges, density).

Employed 10-fold cross-validation and precision-recall area under the curve (PR AUC) for evaluation, with external validation on five independent datasets.

Main Results:

Item-level models outperformed the PHQ-8 total-score baseline.
Network-augmented XGBoost demonstrated the strongest performance, achieving a PR AUC of 0.37.
The model met prespecified screening criteria (recall ≥0.80, specificity ≥0.50) and highlighted key symptom interrelations.

Conclusions:

Integrating symptom-network features with machine learning enhances interpretability and discrimination.
The optimized model surpasses the PHQ-8 total-score baseline and meets pragmatic screening criteria.
This approach is suitable for first-line case finding of suicidal ideation.