Predicting Ultra-High Risk Outcomes Using Linguistic and Acoustic Measures From High-Risk Social Challenge Recordings: mHealth Longitudinal Cohort Exploratory Study
Notice bibliographique
Résumé
Background: Early detection of individuals at ultra-high risk (UHR) for psychosis is critical for timely intervention and improving clinical outcomes. However, current UHR assessments, which rely heavily on psychometric tools, often suffer from low specificity. Speech-based machine learning prediction models can potentially be used to improve prognostic accuracy. However, existing studies often used long, open-ended speech tasks, which limit scalability. The High-Risk Social Challenge (HiSoC) is a short 45-second speech task designed to measure social functioning in individuals with UHR. If the HiSoC task is able to capture predictive signals, it may serve as an effective and scalable speech task for future prediction models. Objective: The study aims to explore whether linguistic and acoustic features extracted from the HiSoC task are associated with UHR outcomes and if they are predictive of different UHR outcomes. Methods: Audio recordings of HiSoC task responses were collected from 41 participants with UHR enrolled in the Longitudinal Youth at Risk Study. A total of 12 individuals converted to psychosis, 15 remitted from UHR status, and 14 maintained UHR status. The responses from the converted group were obtained within 12 months of psychosis onset, while the responses from the remitted and maintained groups were collected at baseline. Linguistic features analyzed included words per minute, articulation rate, dysfluency, and sequential coherence. Acoustic features comprised the mean and SD of fundamental frequency, the mean and SD of intensity, and HF500. Feature differential analysis was conducted via multivariate linear regression. Linear support vector machines were trained as outcome prediction models. Nested cross-validation was used to estimate the generalizability error. The models were principally evaluated on balanced accuracy (BA). Results: The converted group exhibited lower words per minute (adjusted P=.02) and higher dysfluency (adjusted P=.004) compared to the remitted group. No significant differences were found in articulation rate, sequential coherence, or acoustic measures across the outcome groups. Two models outperformed random guess, namely the models using linguistic variables (BA 0.741, 95% CI 0.521-0.882) and linguistic and acoustic variables (BA 0.851, 95% CI 0.508-0.944). Conclusions: Linguistic features extracted from a short speech task exhibit a measurable difference between the outcome groups. Our findings support the feasibility of using signals extracted from the HiSoC task recordings to predict remission in participants with UHR.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».