Development of machine learning prediction models for systemic inflammatory response following controlled exposure to a live attenuated influenza vaccine in healthy adults using multimodal wearable biosensors in Canada: a single-centre, prospective controlled trial
Notice bibliographique
Résumé
BACKGROUND: Presymptomatic or asymptomatic immune system signals and subclinical physiological changes might provide a more objective measure of early viral upper respiratory tract infections (VRTIs) compared with symptom-based detection. We aimed to use multimodal wearable sensors, host-response biomarkers, and machine learning to predict systemic inflammation following controlled exposure to a live attenuated influenza vaccine, without relying on symptoms. METHODS: WE SENSE study is a single-centre (McGill University Health Center, Montreal, QC, Canada), prospective controlled trial that recruited healthy adults aged 18-59 years who had not received or were not planning to receive the seasonal influenza vaccine or any other vaccine during the study period. We excluded participants with any infectious symptoms within 7 days before screening. We collected physiological and activity data (eg, heart rate, breathing rate, and acceleration) through continuous monitoring with a smart ring (Oura ring Gen 2, Oura Oy, Finland), smart watch (Biobeat watch, Biobeat Technologies, Israel), and smart shirt (Astroskin-Hexoskin shirt, Hexoskin, Canada) along with high temporal resolution systemic inflammatory biomarker mapping over 12 days (7 days before inoculation and 5 days after). We frequently tested participants both before and after inoculation via PCR for respiratory pathogens, and monitored them via apps for symptoms and free-text annotations. Machine learning algorithms predicting systemic inflammatory surges were trained (35 participants), validated (ten participants), and tested (ten participants) using gradient-boosting techniques. FINDINGS: Between Dec 10, 2021, and Feb, 28, 2022, we enrolled 56 participants, of whom 55 had available data; all 55 participants continuously wore the Oura ring, 54 participants wore the Astroskin-Hexoskin shirt, and 50 wore the Biobeat watch. 27 (49%) participants were female and 28 (51%) were male; 31 (56%) participants were White, eight (15%) were Asian, four (7%) were Black, two (4%) were Latino or Hispanic, and ten (18%) did not disclose. We used model 2, which included handpicked features from the Oura ring night-time data, as the candidate model because it was built on the lowest number of features (more practical). This model predicted inflammatory surges with receiver operating characteristic area under the curve (ROC-AUC) of 0·73 (95% CI 0·71-0·74) for real-time prediction and 0·89 (0·87-0·90) for a 24-h tolerance prediction window (24h-tol) using night-time data from the Oura ring. Incorporating both night-time and daytime data from the Astroskin-Hexoskin shirt yielded ROC-AUC values of 0·73 (0·71-0·75) for real-time and 0·91 (0·90-0·92) for 24h-tol along with improved precision (ie, specificity [0·83, 0·79-0·87] and F1 score [0·65, 0·58-0·71]). The model based on symptoms alone had lower performance, with ROC-AUC values of 0·66 (0·63-0·68) for real-time and 0·79 (0·77-0·82) for 24h-tol. INTERPRETATION: Systemic inflammatory biomarkers coupled with physiological data from wearable biosensors provided rich and objective data from which to train machine learning algorithms to predict systemic inflammation from a low-grade influenza challenge. This approach outperformed symptom-based detection and has the potential to improve detection of VRTIs such as influenza and decrease time to detection, even among asymptomatic people. FUNDING: The Canadian Institutes of Health Research.
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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) | 0,002 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».