Biomedical Text Mining and Information Extraction Using Prompt-Enhanced and LoRA-Adapted Large Language Models

Abstract

Biomedical named entity recognition (NER) and relation extraction (RE) remain challenging because biomedical texts contain ambiguous abbreviations, complex entity boundaries, domain-specific terminology, and implicit relations. This study proposes a prompt-enhanced and QLoRA-adapted large language model framework for biomedical information extraction. For NER, abbreviation-aware prompting supports candidate detection, contextual interpretation, boundary-aware generation, and schema-constrained outputs. For RE, entity markers identify a predefined target pair, while filtered UMLS and MeSH concepts provide concise evidence. DeepSeek-R1-Distill-Qwen-7B is adapted using LoRA over a 4-bit quantized frozen backbone. Experiments cover three NER and three RE datasets. Across three training seeds, the dataset-level macro-average F1 values are 0.909 ± 0.001 for NER and 0.787 ± 0.001 for RE. Seed-balanced paired bootstrap resampling with 10,000 aligned instance-level resamples confirms significant improvements over a matched deterministic simple-prompt baseline on all six datasets after Holm–Bonferroni correction (adjusted p < 0.001), with absolute F1 gains from +0.091 to +0.131. Repeated-run ablations show low variability and complementary contributions from task-structured prompting, knowledge filtering, deterministic validation, and parameter-efficient adaptation.