To provide a comprehensive examination of NLP methods applied specifically to psychiatric clinical notes, focusing on trends, tools, and challenges.
Approach:
Design and Protocol Registration: Conducted in accordance with Joanna Briggs Institute guidance and reported per PRISMA-ScR standards.
Information Sources and Search Strategy: Systematic electronic search across 7 databases, focusing on documents published from January 2021 to December 2025.
Key Findings:
NLP can transform subjective clinical judgment into measurement-based care.
Pretrained language models (PLMs) like BERT and large language models (LLMs) show superior performance in clinical notes analysis.
Clinical applications include summarization, information extraction, and aiding in disease diagnosis.
Interpretation:
The review identifies gaps in existing literature regarding the application of NLP in psychiatric clinical notes.
Limitations:
The review does not include a prospectively registered protocol.
Focus is limited to documents published within a specific timeframe.
Conclusion:
This scoping review aims to elucidate the current state and future directions of NLP in analyzing psychiatric clinical documentation.
Four-day treatment targeting the prefrontal cortex and cerebellum produced larger reductions in suicidal ideation scores than prefrontal stimulation alone in adolescents with major depressive disorder.
