Wearable accelerometry, EDA, and temperature data from 9 students with profound autism, processed with fine-tuned foundation models, enables prediction of challenging behavior episodes up to 10 minutes in advance at AUC-ROC 0.78 in actual classroom sessions.
BMC medical informatics and decision making , 17(1), p.36
2 Pith papers cite this work. Polarity classification is still indexing.
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A provenance-guided multi-agent pipeline with synthetic evaluation suppresses false positives in remote patient monitoring.
citing papers explorer
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Prediction of Challenging Behaviors Associated with Profound Autism in a Classroom Setting Using Wearable Sensors
Wearable accelerometry, EDA, and temperature data from 9 students with profound autism, processed with fine-tuned foundation models, enables prediction of challenging behavior episodes up to 10 minutes in advance at AUC-ROC 0.78 in actual classroom sessions.
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Veritas-RPM: Provenance-Guided Multi-Agent False Positive Suppression for Remote Patient Monitoring
A provenance-guided multi-agent pipeline with synthetic evaluation suppresses false positives in remote patient monitoring.