The paper introduces a benchmark for recommending SNOMED CT concepts from masked clinical contexts, using data from the SNOMED CT Entity Linking Challenge v1.2.1 derived from MIMIC-IV-Note. It evaluates several methods—including a popularity baseline, sparse TF‑IDF prototypes, dense embeddings, and retrieval‑augmented hybrids—finding that sparse TF‑IDF achieves the best performance with Recall@1 of 14.81% and Recall@10 of 33.43%. The study highlights that concept frequency and lexical context strongly influence recommendation quality, with many test pairs involving concepts unseen during training.
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...
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.
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."
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.
arXiv:2609.15713v1 Announce Type: new Abstract: Recent approaches to 30-day hospital readmission prediction rely on pre-trained language models applied to discharge summaries. Although these methods...
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.
The paper introduces a web‑data curation recipe for pretraining medical encoders, addressing the scarcity of large, diverse corpora in dense‑terminology domains like medicine. It proposes two complementary techniques: medical‑term density filtering to select documents rich in medical terminology, and signal‑amplifying rephrasing that uses an LLM to rewrite documents into denser variants with broader entity contexts. Applied to French medical NLP, the recipe produces the FineMed corpus and the DoctoBERT encoder family, achieving state‑of‑the‑art results on the DrBenchmark public benchmark and a proprietary clinical NER task.
arXiv:2608. 00147v1 Announce Type: cross Abstract: Vision-language pretraining learns rich medical image representations from radiology reports, but previous model variants commonly operate within a single shared embedding space, so concept-level structure and interpretability must be recovered post hoc, limiting model transparency and, hence, clinical utility.
arXiv:2609.22239v1 Announce Type: new Abstract: Ambient AI is increasingly adopted in healthcare to automatically generate clinical notes from patient-clinician conversations, with the potential to s...
The paper introduces CAST, a concept-guided artifact suppression tuning framework that uses sparse autoencoders to identify and suppress note-specific artifacts in clinical language models. CAST labels latent features with an LLM-assisted pipeline and ICD‑10 constraints, then fine‑tunes the model while providing post‑hoc per‑concept attributions for auditability. In experiments on MIMIC‑IV discharge‑note mortality prediction, CAST outperforms standard fine‑tuned encoders and competes with strong LLM baselines while offering a feature‑level audit trail of clinical concepts and suppressed artifacts.
arXiv:2607. 14116v1 Announce Type: cross Abstract: Free-form radiology reports contain rich clinical descriptions, yet converting them for reliable segmentation remains challenging due to the inherent variability of natural language.
The paper proposes a behavior-based fusion model that combines large language models (LLMs) with ontology rankers to improve rare-disease diagnosis. By examining ranked lists, agreement, and ontology support, the model learns how much to rely on each system per case, achieving significant recall gains on Phenopacket Store and RAMEDIS benchmarks. Importantly, the fused diagnoses retain candidate-level ontology evidence for inspection.