Machine learning using clinical variables to screen for depression and anxiety in people with early multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Depression and anxiety are more prevalent in people with multiple sclerosis (pwMS) than in the general population and are thus common psychiatric comorbidities in MS. As such, screening for psychiatric comorbidities in pwMS is an important component of MS care. However, large-scale screening efforts using scales that measure depressive symptoms with the goal of identifying a subgroup of pwMS with a high probability of depression, and thus in need of further assessment, have not been successful. Machine learning algorithms using routinely collected clinical information may be able to serve the same purpose without the encumbrance of scale administration and scoring. METHODS: We used baseline clinical and demographic data collected in the Canadian Prospective Cohort Study to Understand Progression in MS (CanProCo). The Patient Health Questionnaire-9 (PHQ-9) was used to screen for symptoms of depression and the Generalized Anxiety Disorder-7 (GAD-7) for symptoms of anxiety. Machine learning with elastic net was used to develop logistic regression models to predict participant scores above traditional cut-off scores ≥10 that are indicative of clinically significant depression and anxiety. RESULTS: Machine learning with elastic net produced a model that was able to predict scores ≥10 on the PHQ-9 in participants in the testing dataset with a high area under the curve (AUC = 0.927). Prediction of scores ≥10 on the GAD-7 in participants in the testing dataset was modest (AUC = 0.813). Final multivariable logistic regression models found that increased self-reported psychosocial fatigue (OR: 1.55, p < 0.001 and OR: 1.27, p = 0.0026), increased self-reported cognitive fatigue (OR: 1.08, p < 0.001 and OR: 1.08, p < 0.001), and number of comorbidities (OR: 1.26, p = 0.0015 and OR: 1.15, p = 0.041) were predictive of scoring above the cut-off on PHQ-9 and GAD-7, respectively. CONCLUSION: The identification of psychiatric comorbidities such as depression in pwMS could be facilitated by making use of clinical variables with similar success to direct administration of rating scales.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".