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.
By Rakib Abdullah, Md. Maruful Islam Maruf
CardioLM introduces a multilingual suite of small language models tailored for cardiology, comprising CardioBERTa encoders and CardioNER NER models trained on the CardioCCC corpus of 508 cardiology case reports translated into seven languages. The study shows that specialty‑specific pretraining reduces pseudo‑perplexity significantly but only marginally improves strict span‑level F1, whereas joint multilingual training with XLM‑RoBERTa‑large yields the best F1 scores across languages and entity types. Clinicians rated CardioNER far superior to rule‑based baselines, indicating practical value for multi‑country clinical information extraction.
By Manuela-Daniela Danu, Miguel Rodr\'iguez-Ortega, Alberto Becerra-Tome, Pablo Arancibia, Fernando Gallego-Donoso, Lorenzo Pratesi, Xi Bai, Andrei-Damian Dinescu, Salvador Lima-L\'opez, Andrea Guala, Saskia Haitjema, Jonas Mack, Michele Miranda, Wesam Al Nabki, Jan Rodr\'iguez-Miret, Vasile George Marica, Vladim\'ir Kincl, Kate\v{r}ina Dost\'alov\'a, Michael Andrej, Alicia Ujil, Corinne Allaart, Machteld Boonstra, Malin Overmars, Anoop Shah, Giovanna Ceroni, Jonathan Kossoff, Domenico D'Amario, Renzo Laborante, Antonio Iaconelli, Isabella Fumarulo, Jacopo Lenkowicz, Livia Lilli, Emma Weiss, Alex Scafa, Felix Lindberg, Milou Gamage, Eul\`alia Farr\'e-Maduell, S\'ebastien Brati\`eres, Martin Krallinger, Bram van Es
arXiv:2608.29890v1 Announce Type: new
Abstract: Biomedical Named Entity Recognition (NER) is fundamental to healthcare AI applications, including clinical decision support and medical information ext...
By Nhu Vo, Phuong Nguyen, Nu Uyen Phuong Le, Inigo Jauregi Unanue, Dung D. Le, Massimo Piccardi, Wray Buntine
EHRAdapt is an adapter that maps electronic health record tuples directly into a frozen language model’s embedding space, using learned embeddings for modality, time gaps, and dedicated vectors for event codes. It represents each event vector as a sum of a frozen, ontology‑trained semantic prior and a low‑rank evidence residual, enabling robust learning even for rare clinical events. Experiments on four million patient records with three large language model backbones show that the full adapter outperforms all ablations in next‑event prediction and improves rare‑disease discrimination in classification tasks.
By Andre R Goncalves, Vincent Liu, Priyadip Ray