ArticleMethodsX2026
AI-powered mental health application with data privacy preservation.
Article in MethodsX, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Anxiety disorders, emotional conditions, and stress are becoming an increasing tendency in modern society, which makes digital mental health technologies that provide support in these cases highly demanded. This paper focuses on proposing an AI-driven framework that combines Natural Language Processing (NLP) with privacy-protective systems to recognize emotions in the text of the user. The AES-256 encryption, Supabase authentication, and role-based access control are used to secure data security. It is presented as a multi-label problem of emotion classification, which is fine-tuned on the BERT-base-uncased and ModernBERT Large transformer. ModernBERT exhibited rapid convergence and enhanced situational sensitivity especially when it comes to sarcasm and mixed emotional states. Key contributions include:•Planning an emotion feedback loop offering contextual response and customized mindfulness teaching.•Performing a Class imbalance analysis of emotions and trade-offs in precision and recall.•Illustrating privacy-first architecture that is able to integrate with digital-therapy and clinician assisted decision software.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.