Diagnosing Suicidal Ideation from Resting State EEG Data Using a Machine Learning Algorithm
Bibliographic record
Abstract
Suicide poses a global health crisis with significant social and economic impact. Prevention may be possible if objective quantitative methods are developed to supplement the often inaccurate interview-based risk assessments. Our research goal is to develop a machine learning algorithm (MLA) to predict the presence of suicide ideation from resting state electroencephalography (EEG) data collected from 224 subjects with major depressive disorder (MDD) in the Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC) study. Using the Concise Health Risk Tracking Self-Report (CHRT-SR14) questionnaire, 194 subjects acknowledged having suicidal ideation (group 1) and 30 did not (group 2). We balanced the database by matching 30 subjects from group 1 using propensity score analysis. A four-step prediction algorithm was then applied to the selected data including 1) EEG data preprocessing, 2) brain source localization (BSL) using the robust exact low-resolution electromagnetic tomography (ReLORETA) method, 3) determining the connectivity between the brain regions using symbolic transfer entropy (STE), 4) applying MLA to the STE features. Three common classifiers, Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN) were used in this study. Using 70% of the data for training and evaluation and 30% for testing, all three classifiers delivered a high accuracy, where the highest performance belonged to SVM with 88.9% accuracy. These findings support the potential utility of ML analysis of EEG data as a non-verbal way to enhance the accuracy of suicide risk evaluation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".