Spoken language-based automatic cognitive assessment of stroke survivors
Bibliographic record
Abstract
Stroke survivors (SSs) often experience cognitive decline following their initial stroke, necessitating repeat post-stroke cognitive assessments. Current methods of assessment, such as the pen-and-paper-based Montreal Cognitive Assessment (MoCA), is time-consuming and often reliant on seeing skilled clinicians in person. This is at a time when patients have a lot of often diverse rehabilitation needs. To address these challenges, our paper introduces the first system of its kind to be used for this cohort. CognoSpeak is an automated cognitive assessment system that people can use initially on the ward immediately post-stroke (baseline) and subsequently at home (follow-ups). CognoSpeak assesses cognitive decline by asking users to engage with a virtual agent by answering questions and completing clinically-motivated tasks and cognitive tests. The system then uses AI to extract and process speech, language, and interactional cues for cognitive decline. The system was originally developed for dementia; here, we show that it can successfully predict MoCA scores (regression) and identify cognitive decline predicated on a MoCA-based threshold (classification) in the stroke survivor cohort. We explore an extensive set of acoustic- and text-based features as well as different machine learning models. Leveraging a unique dataset of 55 SS CognoSpeak interactions, our findings show excellent performance for both regression and classification style prediction with the best regression result (Normalised Root Mean Squared Error (N-RMSE)) of 0.092. In addition, we show that direct classification of the MoCA score cutoff of 26 yields an F1-score of 0.74 (Specificity: 0.73, Sensitivity: 0.75) using a Logistic Regression Classifier. This demonstrates the first evidence of the system’s robustness and clinical potential.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".