arXiv:2605. 03301v2 Announce Type: replace-cross Abstract: De-identification of clinical text is a prerequisite for the secondary use of electronic health records.
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MedDeID is an on‑premises framework that combines in‑house annotation, synthetic‑note generation, model training, inference, pseudonymisation and evaluation to de‑identify clinical text. On a Dutch hospital benchmark, a hospital‑trained transformer detected 98.9 % of identifying text while redacting only 0.24 % of non‑identifier text; a synthetic‑only model achieved 96.1 %. In primary‑care notes, the synthetic‑trained model outperformed the hospital‑trained model in recall and robustness to identifier‑format changes, and an English version trained without real text reached 99.7 % and 98.9 % detection on synthetic benchmarks.
arXiv:2609.12544v1 Announce Type: new Abstract: Clinical de-identification relies on accurately identifying personally identifiable information (PII). However, manually annotated datasets are costly...
The paper introduces TRACE, a method that removes duplicated text—known as note bloat—from clinical notes by leveraging EHR metadata and frequency-based de‑duplication. Across 5.3 million notes from diverse patient cohorts, TRACE eliminated 47.3 % of chart text while preserving information extraction and prediction performance, with only 0.3–6.6 % of removed content being author‑generated. The authors project that applying TRACE could yield net savings of $1.00 M to $13.58 M over three years at a large academic center, depending on model pricing schemes.
The paper presents a method for extracting key information from OCR‑digitized clinical reports, addressing challenges posed by heterogeneous documents and noisy OCR output. It introduces an open key space that is iteratively mined, normalized, clustered, and verified to build a canonical key inventory, and defines key coverage as a metric for inventory completeness. Experiments on reports from over 20 hospitals using a 0.2B BERT model show that performance improves steadily with key coverage, achieving high F1 scores when the top 90 keys are covered and outperforming a fine‑tuned Qwen3‑0.6B baseline.
This study benchmarks transformer models for Bangla medical named entity recognition (NER), comparing BanglaBERT, multilingual BERT (mBERT), XLM‑RoBERTa, and GPT‑4o mini under zero‑shot and few‑shot prompting. Across a full test set of 3,179 samples, fine‑tuned XLM‑RoBERTa achieves a new state‑of‑the‑art F1‑score of 0.5959, while BanglaBERT lags with 0.4937, suggesting that domain diversity outweighs language specificity. The analysis shows high performance on Medicine and Specialist entities (F1 > 0.83) but lower accuracy on Symptoms (F1 0.4367), and demonstrates that fine‑tuned transformers outperform prompt‑only approaches by a factor of 3.76.
The study evaluates whether large language models (LLMs) with in‑context learning can better identify institution‑specific protected health information (PHI) in electronic health records than existing de‑identification systems. Using 100 pediatric oncology notes from Texas Children’s Hospital, eight LLMs were compared to two purpose‑built systems and pattern‑based baselines under three progressively specific prompts. The best LLM achieved an F1 score of 0.918, recovering 79% of previously missed PHI categories and reaching a recall of 0.981 after iterative prompt refinement, demonstrating that calibrated single‑pass prompting can close the institutional PHI gap while balancing precision and recall.