Article

A tool for mapping medical narratives into medical ontologies in low resource settings: A case study for German

2024-06-12

Abstract excerpt

Named Entity Recognition (NER) is extremely relevant in the clinical field since it allows the extraction of information, such as diagnoses or medical procedures, from non-structured data (doctor’s letters, vignettes, etc.) and coding them based on international classification systems. As a result, language models should be trained to recognize and classify these items accurately. While Large Language Models (LLMs...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: f4225825-089a-56fe-a431-65cf0e266f3e
DOI: 10.1101/2024.06.11.24307163

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

A tool for mapping medical narratives into medical ontologies in low resource settings: A case study for GermanDOI 10.1101/2024.06.11.24307163Read

mCodeGPT: Bridging the Gap between Unstructured Medical Text and Structured Cancer OntologiesSemantic proximity Read

The Aluminum Standard: Using Generative Artificial Intelligence Tools to Synthesize and Annotate Non-Structured Patient DataSemantic proximity Read

Automated Clinical Annotation of Lung Cancer Reports Using Transformer-Based NLP Models: A Benchmarking and Fine-Tuning Study on a Novel Tunisian Clinical CorpusSemantic proximity Read

Europe PMC Annotated Full-text Corpus for Gene/Proteins, Diseases and OrganismsSemantic proximity Read

Towards a natural language processing pipeline and search engine for biomedical associations derived from scientific literatureSemantic proximity Read

Accurate Clinical Entity Recognition and Code Mapping of Anatomopathological Reports Using BioClinicalBERT Enhanced by Retrieval-Augmented Generation: A Hybrid Deep Learning ApproachSemantic proximity Read

A hybrid framework with large language models for rare disease phenotyping.Semantic proximity Read

NERO: A Biomedical Named-entity (Recognition) Ontology with a Large, Annotated Corpus Reveals Meaningful Associations Through Text EmbeddingSemantic proximity Read

Hospital-wide Natural Language Processing summarising the health data of 1 million patientsSemantic proximity Read

CancerBERT: a cancer domain-specific language model for extracting breast cancer phenotypes from electronic health records.Semantic proximity Read

Advancements in Multilingual Biomedical Natural Language Processing: Exploring Large Language Models for Named Entity Recognition and LinkingSemantic proximity Read

BioALBERT: A Simple and Effective Pre-trained Language Model for Biomedical Named Entity RecognitionSemantic proximity Read