- Article
- Open access
- Published:
- Harry Kam Hung Tsui1 na1,
- Sophia Vann-Adibe1,
- Wing Yan Vivian Tsang1,
- Huiquan Zhou1,
- So Hon-Cheong2,
- Qingpeng Zhang3,4 &
- …
- Sherry Kit Wa Chan1,5
npj Digital Medicine (2026) Cite this article
We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.
Abstract
Natural language processing (NLP) enables the use of electronic health records (EHRs) for psychosis research in real-world settings and develops precision psychiatry. We conducted a PRISMA-guided systematic review of five databases from inception to 9 October 2025 and identified 62 eligible studies with over 1 million participants. Most studies (62.9%) used data from the UK Clinical Record Interactive Search (CRIS) platform. Methodologically, 37.1% applied dictionary-based NLP, 53.2% representation-based machine learning, 9.7% transformer-based NLP, and none applied generative large language models (LLMs). Clinically, 74.2% targeted symptom/information extraction, 19.2% classification, and 16.1% risk prediction. However, 56.5% studies self-reported limited generalizability, 32.3% reported data quality/collection challenges, and only two studies performed external validation. Despite promising proof-of-concept performance, evidence of real-world clinical utility remains limited. Current work remains concentrated on retrospective phenotyping within a single healthcare ecosystem. We highlight priorities for multi-system validation, temporal trajectory modeling, and responsible LLMs deployment in psychosis EHRs research.
Subjects
Acknowledgements
This study was supported by the Basic Research Seed Fund of The University of Hong Kong (Reference numbers: 109000324 and 104006611) and the Health and Medical Research Fund (HMRF; Reference number: 20212521) awarded to Sherry Kit Wa Chan.
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissions
About this article
Cite this article
Ho, R.H.L., Tsui, H.K.H., Vann-Adibe, S. et al. Natural language processing application in electronic health records studies for psychosis: a systematic review. npj Digit. Med. (2026). https://doi.org/10.1038/s41746-026-03125-z
Download citation
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41746-026-03125-z