Deep Learning Analysis of Trauma-Related Emotional Patterns in Couples Facing PTSD
Bibliographic record
Abstract
This study aimed to examine and classify trauma-related emotional patterns in couples facing PTSD using multimodal deep learning models to predict high-risk emotional episodes during conflict interactions. The study employed a cross-sectional design involving 112 couples (224 individuals) from the United States in which one partner met diagnostic criteria for PTSD. Each couple participated in a 40-minute conflict-discussion task recorded through synchronized audio, video, and physiological sensors. Linguistic transcripts, facial expressions, acoustic features, and autonomic indicators (electrodermal activity, heart-rate variability, and peripheral temperature) were extracted and temporally aligned. These modalities were analyzed using a multimodal transformer architecture integrating text embeddings, CNN-LSTM visual features, physiological time-series data, and acoustic spectral representations. Additional analyses included t-SNE–based latent clustering of emotional patterns and risk-surface modeling of predicted high-risk episodes as a nonlinear function of physiological arousal and negative emotional language. Model performance was evaluated using accuracy, precision, recall, F1-score, and AUC metrics with ten-fold stratified cross-validation. The multimodal transformer significantly outperformed baseline and unimodal models in predicting high-risk PTSD-related emotional episodes (AUC = .90; F1 = .83; accuracy = .86; p < .001 vs. clinical baseline). All unimodal models performed above chance but remained significantly weaker than the multimodal approach (p < .01). Latent clustering revealed four statistically distinct emotional interaction patterns (hyperaroused escalation, avoidant disengagement, mixed volatile–repairing, and numbed coexistence), each differing significantly in PTSD severity, relationship quality, and proportion of high-risk segments (all ps < .05). Nonlinear risk-surface analysis demonstrated a strong interaction effect between negative emotional language and physiological arousal in predicting emotional risk (βinteraction = .41, p < .001). Multimodal deep learning provides a highly sensitive and integrative method for identifying trauma-related emotional risk states in couples facing PTSD, revealing clinically meaningful emotional clusters and nonlinear escalation patterns that may inform assessment and intervention.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".