{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:43:22Z","timestamp":1785545002505,"version":"3.56.0"},"reference-count":187,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Med Inform"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec sec-type=\"background\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Psychiatric clinical notes in electronic health records (EHRs) provide rich longitudinal information that can support clinical decision-making. Using historical medical data can enable earlier identification of mental illness, better characterization of disease trajectories, and more personalized treatment planning. Natural language processing (NLP) transforms these unstructured notes into analyzable representations for research and care.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"objective\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aims to systematically summarize NLP methodologies for psychiatric clinical notes, compare major modeling paradigms and application areas, and highlight emerging large language model (LLM) trends, key challenges, and future research directions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"methods\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, a literature search was conducted for articles on NLP methods based on psychiatric clinical notes published from January 2021 to December 2025 in Ovid MEDLINE, Ovid EMBASE, PubMed, Scopus, Web of Science, the ACM Digital Library, and ScienceDirect. This scoping review analyzed NLP methods applied to psychiatric clinical notes, focusing on major trends, identifying suitable features for traditional machine learning (ML)\u2013based models, applications of pretrained language models (PLMs), and key challenges. Approaches were categorized as rule-based, traditional ML, hybrid, deep learning (DL), and LLM-based methods across information extraction and text classification tasks.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"results\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In total, 101 studies were eligible for inclusion. Rule-based methods (n=36) and hybrid approaches (n=34) remained the most widely used techniques, largely favored for their interpretability in handling nuanced, subjective clinical notes. These were followed by DL (n=15), traditional ML (n=10), and LLM-based approaches (n=6). Traditional ML studies relied heavily on engineered features, which could be grouped into 5 broad categories: domain knowledge features, lexical and statistical features, vector-based semantic features, emotion-related features, and temporal features. PLMs improved performance mainly through domain adaptation and task-specific fine-tuning, enhancing the handling of psychiatric language, medical terminology, and clinical note structure. LLM-based studies, although still limited in number, indicated a growing shift toward generative and reasoning-based applications.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec sec-type=\"conclusions\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Hybrid NLP approaches remain dominant, combining domain rules with ML for extraction and classification. DL approaches continue to advance, with domain adaptation supporting medical terminology and clinical semantics. LLMs may further automate complex workflows via zero-shot capabilities and reasoning, alongside growing interest in temporal modeling and multimodal integration. Key future needs include improved generalizability across institutions, privacy protection, and careful attention to ethical implications in clinical deployment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2196\/91249","type":"journal-article","created":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T20:44:02Z","timestamp":1783716242000},"page":"e91249-e91249","source":"Crossref","is-referenced-by-count":0,"title":["Natural Language Processing Applied to Psychiatric Clinical Notes: Scoping Review"],"prefix":"10.2196","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-2348-8223","authenticated-orcid":false,"given":"Shuying","family":"Rao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5061-4699","authenticated-orcid":false,"given":"Xi'ang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guifeng","family":"Deng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4198-1991","authenticated-orcid":false,"given":"Junyi","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tiecheng","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaoyun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0739-8413","authenticated-orcid":false,"given":"Haiteng","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,7,10]]},"reference":[{"issue":"2","key":"R1","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1007\/s11920-019-0997-0","article-title":"Global burden of disease and the impact of mental and addictive disorders","volume":"21","author":"Rehm","journal-title":"Curr Psychiatry Rep"},{"issue":"10312","key":"R2","doi-asserted-by":"publisher","first-page":"1700","DOI":"10.1016\/S0140-6736(21)02143-7","article-title":"Global prevalence and burden of depressive and anxiety disorders in 204 countries and territories in 2020 due to the COVID-19 pandemic","volume":"398","author":"Santomauro","journal-title":"Lancet"},{"issue":"13","key":"R3","doi-asserted-by":"publisher","first-page":"1215","DOI":"10.1001\/jama.2017.11295","article-title":"Digital phenotyping: technology for a new science of behavior","volume":"318","author":"Insel","journal-title":"JAMA"},{"key":"R4","doi-asserted-by":"publisher","DOI":"10.2147\/RMHP.S12985","article-title":"Benefits and drawbacks of electronic health record systems","volume":"4","author":"Menachemi","journal-title":"Risk Manag Healthc Policy"},{"issue":"1","key":"R5","doi-asserted-by":"publisher","first-page":"48","DOI":"10.7326\/M13-1531","article-title":"Health information technology: an updated systematic review with a focus on meaningful use","volume":"160","author":"Jones","journal-title":"Ann Intern Med"},{"issue":"5","key":"R6","doi-asserted-by":"publisher","first-page":"760","DOI":"10.1016\/j.jbi.2009.08.007","article-title":"What can natural language processing do for clinical decision support?","volume":"42","author":"Demner-Fushman","journal-title":"J Biomed Inform"},{"key":"R7","unstructured":"Manning C Schutze H . 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