Persona-Driven Data Augmentation for Disease Name Recognition Across Rare and General Disease Corpora: Comparative...

Medical information extraction requires automatically identifying disease names and related terms in text. This task, known as named entity recognition (NER), relies on expert-annotated data that are costly to produce and often available only in limited quantities. Data augmentation (DA) aims to expand available training data; however, standard techniques such as synonym replacement and back-translation may introduce inappropriate substitutions or fail to preserve entity-label alignment, which is critical for sequence-labeling tasks. Although large language models can generate fluent text, their outputs may also contain factual inconsistencies or unintended changes if not carefully controlled.

This study investigated whether persona-driven, document-level DA using a large language model could improve biomedical disease NER performance by generating diverse rephrasings of medical documents while preserving annotated entities.

We designed a DA framework using multiple personas that varied in medical expertise, personality, tone, and narrative style. Using prompting constrained by XML tags, each persona rephrased training documents while aiming to preserve annotated entity spans. We evaluated the framework on 2 biomedical disease NER datasets with complementary roles: RareDis, a low-resource rare disease corpus, and National Center for Biotechnology Information (NCBI) disease, a more general disease benchmark. Semantic fidelity and lexical diversity were measured using BERTScore and Bilingual Evaluation Understudy (BLEU-4), respectively, and personas were grouped into high-, balanced-, and low-fidelity subsets. Biomedical pretrained BioBERT models were fine-tuned and evaluated under multiple settings, including gold-standard (GS) data only, synonym replacement, single-persona augmentation, curated persona subsets, and all-persona augmentation. Performance was assessed using microaveraged entity-level precision, recall, and F₁-score, and results were examined at both the overall and individual entity-type levels. Performance values are reported as mean (SD).

Persona-driven DA improved NER performance over GS-only training in both datasets, with the strongest gains obtained by combining multiple persona-generated variants with GS data. In RareDis, the best result was achieved by the low-fidelity subset (mean F₁-score 73.35, SD 0.19 vs baseline 71.22, SD 0.45), while in NCBI disease, the all-personas setting performed best (mean F₁-score 89.32, SD 0.26 vs baseline 87.82, SD 0.18). In low-resource experiments, the all-personas and high-fidelity persona settings in NCBI disease exceeded the performance of the model trained on 100% GS data using only 60% of the training data, whereas gains in RareDis were more modest. Entity-level analysis showed improvements across RareDis categories, particularly for symptom, and confusion analysis indicated reduced symptom-sign confusion under augmentation.

Persona-driven DA improved biomedical disease NER by introducing controlled linguistic variation while largely preserving annotated entities. The strongest gains were obtained when multiple persona-generated variants were combined with GS data, although the benefit varied across datasets. These findings suggest that this approach is a promising strategy for low-resource biomedical NER.