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
arXiv:2609.07986v1 Announce Type: new
Abstract: Purpose: Accurate CT protocol selection is critical for diagnostic quality and patient safety, yet the current process is manual, time-consuming, and p...
By Zahra Hosseini, Mahan Pouromidi, Farzad Khalvati, Patrik Rogalla
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
This thesis explores how to select and adapt NLP models for global health literature when annotated data and computational resources are scarce. It compares skip‑gram word2vec models trained on increasingly large specialized corpora with BioWordVec for semantic tag discovery, finding that larger coverage does not always yield more useful domain associations. The study also evaluates convolutional spaCy models versus a RoBERTa transformer for named entity recognition, noting a trade‑off between higher F1 scores and longer inference time, and investigates MiniLM few‑shot versus BART‑MNLI zero‑shot classification for multi‑label topic classification, highlighting practical constraints of inference cost.
"whyItMatters":"The work provides empirical guidance on balancing model accuracy and resource demands for building knowledge systems in low‑resource global health settings."
By Genis Skura, Antoine Geissb\"uhler, Jean-Luc Falcone
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
The paper introduces ICD-Deepresearch, a workflow that combines foundation models for electronic health records (EHR) and language models with medical search and ICD dictionaries to forecast future ICD codes for upcoming clinical encounters. It evaluates candidate code transitions by linking patient evidence, external clinical relations, and exact code semantics within a fixed top‑K budget, using SparseEHR for initial priors, GPT‑5 for complementary forecasts, and a final selection step that validates, deduplicates, and ranks candidates. The method achieves patient‑averaged precision/recall of 24.60/35.09% on MIMIC‑III and 25.14/48.32% on MIMIC‑IV, with physicians rating 51–68% of its retrieved documents as useful, outperforming standalone GPT‑5 web search and Medical Deep Research.
By Junda Wang, Meysam Ghaffari, Akshat Choube, Mohsen Sharifi Renani, Hong Yu, Carlos Morato