arXiv Machine Learning

Automatic identification of diagnosis from hospital discharge letters via weakly supervised Natural Language Processing

arXiv:2410. 15051v3 Announce Type: replace-cross Abstract: Identifying patient diagnoses from hospital discharge letters is essential for large-scale cohort selection and epidemiological research, but traditional supervised approaches require extensive manual annotation, which is often impractical for large textual datasets.

arXiv Computation and Language

Sep 22

Custom Named Entity Recognition and Topic Classification for Global Health Publications

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

llms nlp fine-tuning

arXiv AI

Jun 18

Language Models as Interfaces, Not Oracles: A Hybrid LLM-ML System for Pediatric Appendicitis

arXiv:2606. 19183v1 Announce Type: cross Abstract: Large language models (LLMs) can make clinical decision support more accessible by interpreting free-text documentation, but their direct use as diagnostic engines is limited by sensitivity to prompts, information order, and plausible but incorrect outputs.

By Soheyl Bateni, Maryam Abdolali

llms safety

arXiv Machine Learning

Sep 25

Language Specificity vs. Domain Diversity: Benchmarking Transformers for Bangla Medical NER

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

llms nlp fine-tuning benchmarks

arXiv AI

Jul 3

eCream-MedCorpus A Large-Scale Corpus of Clinical Notes for Italian

arXiv:2606. 12569v2 Announce Type: replace-cross Abstract: We present eCream-MedCorpus, a new and unique large-scale dataset of clinical notes produced in Emergency Departments of Italian hospitals.

By Tiziano Labruna, Guido Bertolini, Pietro Ferrazzi, Bernardo Magnini

llms benchmarks

arXiv AI

Jul 8

eCREAM-MedCorpus A Large-Scale Corpus of Clinical Notes for Italian

arXiv:2606. 12569v3 Announce Type: replace-cross Abstract: We present eCREAM-MedCorpus, a new and unique large-scale dataset of clinical notes produced in Emergency Departments of Italian hospitals.

By Tiziano Labruna, Guido Bertolini, Pietro Ferrazzi, Bernardo Magnini

llms benchmarks

arXiv AI

Jun 12

EDEN: A Large-Scale Corpus of Clinical Notes for Italian

arXiv:2606. 12569v1 Announce Type: cross Abstract: We present EDEN (Emergency Department Electronic Notes), a new and unique large-scale corpus of clinical notes produced in Emergency Departments of Italian hospitals.

By Tiziano Labruna, Guido Bertolini, Pietro Ferrazzi, Bernardo Magnini

llms benchmarks

arXiv AI

Sep 1

Standardizing Longitudinal Radiology Report Evaluation via Large Language Model Annotation

arXiv:2601.16753v2 Announce Type: replace-cross Abstract: Longitudinal information in radiology reports refers to the sequential tracking of findings across multiple examinations over time, which is...

By Xinyi Wang, Grazziela Figueredo, Ruizhe Li, Xin Chen

llms benchmarks

arXiv AI

Jul 2

SHIELD: A Diverse Clinical Note Dataset and Distilled Small Language Models for Enterprise-Scale De-identification

arXiv:2605. 03301v2 Announce Type: replace-cross Abstract: De-identification of clinical text is a prerequisite for the secondary use of electronic health records.

By Jose D. Posada, David Love, Somalee Datta, Priya Desai

llms efficiency benchmarks

arXiv AI

Aug 18

Toward Better Assessment of LLMs' Performance in Clinical Error Detection

arXiv:2608. 16643v1 Announce Type: cross Abstract: Automated detection of errors in clinical documentation is a promising application of large language models (LLMs), yet decisions to deploy such models rest on benchmarks that evaluate each clinical note in isolation.

By Yifan Zhang, Rahmatollah Beheshti

llms nlp benchmarks safety

arXiv Machine Learning

Sep 15

Knowledge-Enriched Structured EHR Features for 30-Day Hospital Readmission Prediction on MIMIC-IV

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...

By Mohamad Najafi, Hongyun Fu, Mathias Brochhausen, Jian Wu, Yaohang Li

llms rag safety

arXiv Computation and Language

Sep 14

Meddies-PII: A Multilingual Framework for Personally Identifiable Information Extraction in Clinical De-identification

arXiv:2609.12544v1 Announce Type: new Abstract: Clinical de-identification relies on accurately identifying personally identifiable information (PII). However, manually annotated datasets are costly...

By Linh Uyen Le, Christian Hoang, Huy Hoang Ha

benchmarks

arXiv Machine Learning

Sep 22

Clinical Domain Classification from Medical Transcriptions

arXiv:2609.22734v1 Announce Type: cross Abstract: Clinical domain classification plays an important role in organizing and analyzing large volumes of unstructured medical text. However, medical trans...

By Sravani Pottipati, Lakshmikar R. Polamreddy

llms nlp

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