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Related Experiment Video

Updated: Jul 12, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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Published on: September 20, 2018

Natural Language Processing Applied to Psychiatric Clinical Notes: Scoping Review.

Shuying Rao1,2,3,4, Xi'ang Chen1,2,3,4, Guifeng Deng1,2,3,4

  • 1School of Brain Science and Brain Medicine, and Liangzhu Laboratory, Zhejiang University School of Medicine, Affiliated Mental Health Center and Hangzhou Seventh People's Hospital, 305 Tianmushan Road, Xihu District, Hangzhou, Zhejiang, 310058, China, 86 0571-87071107.

JMIR Medical Informatics
|July 10, 2026
PubMed
Summary
This summary is machine-generated.

Natural language processing (NLP) methods are advancing for psychiatric clinical notes, with hybrid approaches dominating. Emerging large language models (LLMs) show promise for future applications in mental healthcare.

Keywords:
electronic health recordsinformation extractionlarge language modelsnatural language processingpsychiatric clinical notes

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Area of Science:

  • Computational linguistics
  • Medical informatics
  • Psychiatry

Background:

  • Electronic health records (EHRs) contain valuable longitudinal data for psychiatric care.
  • Natural Language Processing (NLP) is crucial for analyzing unstructured clinical notes.
  • Historical data aids in early mental illness identification and personalized treatment.

Purpose of the Study:

  • To systematically review NLP methodologies for psychiatric clinical notes.
  • To compare NLP modeling paradigms and application areas.
  • To highlight emerging trends, challenges, and future directions, including large language models (LLMs).

Main Methods:

  • Scoping review following PRISMA-ScR guidelines.
  • Literature search from January 2021 to December 2025 across major databases.
  • Analysis of NLP methods categorized into rule-based, traditional ML, hybrid, deep learning (DL), and LLM-based approaches.

Main Results:

  • 101 studies were included; hybrid (n=34) and rule-based (n=36) methods were most common.
  • Traditional ML utilized engineered features (domain, lexical, semantic, emotion, temporal).
  • Pretrained language models (PLMs) enhanced performance via domain adaptation and fine-tuning; LLMs show potential for generative tasks.

Conclusions:

  • Hybrid NLP methods remain dominant for psychiatric note analysis.
  • Deep learning and LLMs offer advancements in handling clinical semantics and complex workflows.
  • Future research should focus on generalizability, privacy, and ethical considerations for clinical deployment.