Using Digital Phenotypes to Identify Individuals With Alexithymia in Posttraumatic Stress Disorder: Cross-Sectional Study (Preprint)
Bibliographic record
Abstract
BACKGROUND Alexithymia, defined as difficulty identifying and describing one’s emotions, has been identified as a transdiagnostic emotional process that impacts the course, severity, and treatment outcomes of psychiatric conditions such as posttraumatic stress disorder (PTSD). As such, alexithymia is an important process to accurately measure and identify in clinical contexts. However, research identifying the association between the experience of alexithymia and psychopathology has been limited by an overreliance on self-report scales, which have restricted use for measuring constructs that involve deficits in self-awareness, such as alexithymia. Hence, more suitable and effective methods of measuring and identifying those experiencing alexithymia in clinical samples are needed. OBJECTIVE In this cross-sectional study, we aimed to determine if facial, vocal, and language phenotypes extracted from 1-minute recordings of war veterans with PTSD describing a traumatic event could be used to identify those experiencing alexithymia. METHODS A total of 96 participants were included in this cross-sectional study. Specialized software was used to extract facial, vocal, and language features from the recordings. These features were then integrated into machine learning (extreme gradient boosting [XGBoost]) classification models that were trained and tested within a 5-fold nested cross-validation pipeline for their capacity to classify veterans scoring above the cutoff for alexithymia on the Toronto Alexithymia Scale-20. RESULTS The best performing XGBoost classification model trained in the nested cross-validation pipeline was able to classify those experiencing alexithymia with a good level of accuracy (average F1-score=0.78, SD 0.07; average area under the curve score=0.87, SD 0.12). Consistent with theoretical models and past research into phenotypes of alexithymia, language, vocal, and facial features all contributed to the accuracy of the XGBoost classification model. CONCLUSIONS These findings indicate that facial, vocal, and language phenotypes incorporated in machine learning models could represent a promising alternative to identifying individuals with PTSD who are experiencing alexithymia. The further validation and use of this approach could facilitate more tailored and effective allocation of treatment resources to individuals experiencing alexithymia in clinical settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".