ArticleNPJ digital medicine2025
Leveraging large language models for the deidentification and temporal normalization of sensitive health information in electronic health records.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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
5 citing papers in PubMed.
- From Medical Records to AI-Ready Datasets: A Practical Guide for Clinical Researchers.Journal of clinical medicine · 2026Article
- Benchmarking large language models for de-identification of electronic health record notes.BMJ health & care informatics · 2026Article
- Evaluating real-world deployment of an HL7-CDA-aligned LLM for ICD-10-CM coding.NPJ digital medicine · 2026Article
- Exploring Machine Learning Approaches for Decision Support in Neoadjuvant Therapy of Locally Advanced Rectal Cancer.Oncology research · 2026Article
- Multimodal analysis of whole slide images in colorectal cancer.NPJ digital medicine · 2025Article
Corrections and comments
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Authors and funding
17 authors.
Funding
Abstract
Secondary use of electronic health record notes enhances clinical outcomes and personalized medicine, but risks sensitive health information (SHI) exposure. Inconsistent time formats hinder interpretation, necessitating deidentification and temporal normalization. The SREDH/AI CUP 2023 competition explored large language models (LLMs) for these tasks using 3,244 pathology reports with surrogated SHIs and normalized dates. The competition drew 291 teams; the top teams achieved macro-F1 scores >0.8. Results were presented at the IW-DMRN workshop in 2024. Notably, 77.2% used LLMs, highlighting their growing role in healthcare. This study compares competition results with in-context learning and fine-tuned LLMs. Findings show that fine-tuning, especially with lower-rank adaptation, boosts performance but plateaus or degrades in models over 6 B parameters due to overfitting. Our findings highlight the value of data augmentation, training strategies, and hybrid approaches. Effective LLM-based deidentification requires balancing performance with legal and ethical demands, ensuring privacy and interpretability in regulated healthcare settings.
Identifiers
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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.