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Linguistic Biomarkers of Medication Non-Adherence in Schizophrenia: An NLP-Enhanced Random Forest Analysis of Patient Interviews in Federal Neuropsychiatric Hospital Dawanau, Kano, Nigeria.

This study aims to develop and validate a speech-based digital phenotyping model for predicting medication non-adherence in Hausa-speaking schizophrenia patients. Methodology: We utilize a high-fidelity NLP pipeline involving RecForge II for audio capture and ElevenLabs Scribe for transcription, followed by a clinical pass for tonal accuracy. Analysis: An NLP-enhanced Random Forest classifier will be used to identify linguistic biomarkers (e.g., alogia, tonal flattening) predictive of non-adherence (validated via the MARS scale). Reporting Standards: This project is prospectively committed to …

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Source	Internet Archive
Collection	International records
Kind	Document
Publisher	Center for Open Science
Date	2026-03-24
Type	data
Details	ia title
Found at	https://archive.org/details/osf-registrations-becxq-v1
Archived copy	Look it up in the Wayback Machine
Record ID	`internet-archive:osf-registrations-becxq-v1`

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Linguistic Biomarkers of Medication Non-Adherence in Schizophrenia: An NLP-Enhanced Random Forest Analysis of Patient Interviews in Federal Neuropsychiatric Hospital Dawanau, Kano, Nigeria.. Internet Archive, 2026. Vero Archive, record internet-archive:osf-registrations-becxq-v1.