The paper introduces Distilled Rapid Embedding Transfer (DRET), a parameter‑efficient method that injects biomedical domain knowledge from large specialized models into a smaller general‑purpose model without retraining on the original specialized corpora. DRET evolves through iterative strategies—tokenizer‑merge (DRET 1.x), hybrid embedding averaging (DRET 2.0), priority‑based embedding transfer (DRET 3.x), and further refinements (DRET 4.x)—and demonstrates that a 66‑million‑parameter DistilBERT can achieve competitive or superior performance on token‑level PICO classification compared to much larger models, while remaining lightweight. The authors validate the embedding‑level transfer with cosine similarity, semantic‑shift, and t‑SNE analyses, highlighting DRET’s potential for scalable, resource‑efficient biomedical text mining.
By Girish Sundaram, Daniel Berleant
arXiv:2608. 04144v1 Announce Type: cross Abstract: Biomedical entity linking grounds mentions in clinical and scientific text to entities in a curated knowledge base (KB) with ontological structure, which supports downstream applications such as literature-scale information extraction and patient-record normalization.
By Yicheng Tao, Jie Liu
The paper introduces UdonCare, a hierarchy‑pruning method that iteratively partitions patients into latent domains using medical ontologies, aiming to improve domain generalization in clinical prediction tasks. It addresses challenges of missing domain labels and lack of clinical insight by discovering hierarchy‑grounded patient domains. Experiments on MIMIC‑III, MIMIC‑IV, and eICU datasets show UdonCare outperforms eight baseline methods across four prediction tasks with significant domain gaps.
By Pengfei Hu, Xiaoxue Han, Fei Wang, Yue Ning
arXiv:2609.00228v1 Announce Type: new
Abstract: Scientific domain entity linking (EL) differs from general domain EL because mentions and entity names often lack lexical overlap. Another challenge is...
By Md Rasel Khondokar, Qiao Qiao, Farjana Sultana Samia, Nhat Le, Yuepei Li, Qi Li
arXiv:2608.28607v1 Announce Type: new
Abstract: Pharmaceutical sponsors developing a drug for both the United States and the European Union must reconcile guidance issued independently by the FDA and...
By Chuchu Wu, Zhiyin Zhou, Jingzhuo Hu, Liang You
arXiv:2608. 19201v1 Announce Type: cross Abstract: Bioinformatics software and databases are essential components of modern life science research, yet their mentions in the scientific literature are often inconsistent and difficult to systematically identify at scale.
By Hao Xuan, Rithvij Pasupuleti, Ben Liu, Haishuo Sun, Jun Zhang, Zijun Yao, Cuncong Zhong
BELXTR is a new biomedical entity linking model that uses a multi‑vector (late interaction) architecture to preserve token‑level matching information, unlike traditional embedding‑based approaches that compress mentions into a single vector. By extending the XTR model with a task‑specific training objective and active query expansion, BELXTR achieves state‑of‑the‑art performance on half of ten evaluated corpora, with an average 5‑percentage‑point gain in recall@1. The model shows especially strong results on cross‑species gene disambiguation, outperforming an LLM‑powered retrieve‑and‑rerank pipeline and approaching a specialized rule‑based system.
By Samuele Garda, Ulf Leser
arXiv:2606. 11208v1 Announce Type: cross Abstract: Biomedical findings often seem to conflict across studies, but many of these differences are context-dependent rather than true contradictions.
By Elias Hossain, Sanjeda Sara Jennifer, Sabera Akter Bushra, Niloofar Yousefi
arXiv:2407. 11823v4 Announce Type: replace Abstract: The United States Food and Drug Administration's (FDA's) 510(k) pathway allows manufacturers to gain medical device approval by demonstrating substantial equivalence to a legally marketed device.
By Mohammad Zhalechian, Soroush Saghafian, Omar Robles
BioELX is a retrieve‑rerank framework for cross‑lingual biomedical entity linking that tackles two key problems: the English‑biased UMLS alias training data and the degradation caused by naïvely adding context. It fine‑tunes SapBERT_multi with Wikidata‑derived cross‑lingual alias supervision to create shared concept neighborhoods, and then reranks candidates using pretrained LLMs with mention‑anchored prompting to focus on the target mention. Experiments demonstrate state‑of‑the‑art performance on four benchmarks, improving Recall@1 by 4.8–18.2 percentage points without task‑specific annotations.
By Yi Wang, Corina Dima, Liangyu Zhong, Steffen Staab
arXiv:2606. 31171v1 Announce Type: new Abstract: Acquiring comprehensive cross-domain biomedical profiles is often costly and time-consuming, resulting in severe data scarcity in medical research.
By Mengying Zhou, Yongjie Yin, Haoyan Xin, Guoping Liu, Yang Chen
arXiv:2606. 27174v1 Announce Type: new Abstract: Medical device recalls are a critical regulatory mechanism for protecting patient safety.
By Ali Semih Atalay, Sevgi Yigit-Sert