FRACCO: A gold-standard annotated corpus of oncological entities with ICD-O-3.1 normalisation

• URL: http://arxiv.org/abs/2510.13873v1
• Date: Mon, 13 Oct 2025 14:00:11 GMT
• Title: FRACCO: A gold-standard annotated corpus of oncological entities with ICD-O-3.1 normalisation
• Authors: Johann Pignat, Milena Vucetic, Christophe Gaudet-Blavignac, Jamil Zaghir, Amandine Stettler, Fanny Amrein, Jonatan Bonjour, Jean-Philippe Goldman, Olivier Michielin, Christian Lovis, Mina Bjelogrlic,
• Abstract summary: FRACCO is an expert-annotated corpus of 1301 synthetic French clinical cases.<n>Each document is annotated with terms related to morphology, topography, and histologic differentiation.
• Score: 0.9072519671397496
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Developing natural language processing tools for clinical text requires annotated datasets, yet French oncology resources remain scarce. We present FRACCO (FRench Annotated Corpus for Clinical Oncology) an expert-annotated corpus of 1301 synthetic French clinical cases, initially translated from the Spanish CANTEMIST corpus as part of the FRASIMED initiative. Each document is annotated with terms related to morphology, topography, and histologic differentiation, using the International Classification of Diseases for Oncology (ICD-O) as reference. An additional annotation layer captures composite expression-level normalisations that combine multiple ICD-O elements into unified clinical concepts. Annotation quality was ensured through expert review: 1301 texts were manually annotated for entity spans by two domain experts. A total of 71127 ICD-O normalisations were produced through a combination of automated matching and manual validation by a team of five annotators. The final dataset representing 399 unique morphology codes (from 2549 different expressions), 272 topography codes (from 3143 different expressions), and 2043 unique composite expressions (from 11144 different expressions). This dataset provides a reference standard for named entity recognition and concept normalisation in French oncology texts.

Related papers

• Using LLMs for Multilingual Clinical Entity Linking to ICD-10 [3.7463543521744764]
  We propose an approach for linking clinical terms to ICD-10 codes in different languages using Large Language Models (LLMs)<n>Our system shows promising results in predicting ICD-10 codes on different benchmark datasets in Spanish.
  arXiv  Detail & Related papers  (2025-09-05T07:30:40Z)
• ISPO: An Integrated Ontology of Symptom Phenotypes for Semantic Integration of Traditional Chinese Medical Data [24.36545694430613]
  This study aimed to construct an Integrated Ontology of symptom phenotypes (ISPO) to support the data mining of Chinese EMRs and real-world study in TCM field.
  arXiv  Detail & Related papers  (2024-07-08T15:23:50Z)
• ClinLinker: Medical Entity Linking of Clinical Concept Mentions in Spanish [39.81302995670643]
  This study presents ClinLinker, a novel approach employing a two-phase pipeline for medical entity linking. It is based on a SapBERT-based bi-encoder and subsequent re-ranking with a cross-encoder, trained by following a contrastive-learning strategy to be tailored to medical concepts in Spanish.
  arXiv  Detail & Related papers  (2024-04-09T15:04:27Z)
• PMC-LLaMA: Towards Building Open-source Language Models for Medicine [62.39105735933138]
  Large Language Models (LLMs) have showcased remarkable capabilities in natural language understanding. LLMs struggle in domains that require precision, such as medical applications, due to their lack of domain-specific knowledge. We describe the procedure for building a powerful, open-source language model specifically designed for medicine applications, termed as PMC-LLaMA.
  arXiv  Detail & Related papers  (2023-04-27T18:29:05Z)
• Few-Shot Cross-lingual Transfer for Coarse-grained De-identification of Code-Mixed Clinical Texts [56.72488923420374]
  Pre-trained language models (LMs) have shown great potential for cross-lingual transfer in low-resource settings. We show the few-shot cross-lingual transfer property of LMs for named recognition (NER) and apply it to solve a low-resource and real-world challenge of code-mixed (Spanish-Catalan) clinical notes de-identification in the stroke.
  arXiv  Detail & Related papers  (2022-04-10T21:46:52Z)
• A Unified Framework of Medical Information Annotation and Extraction for Chinese Clinical Text [1.4841452489515765]
  Current state-of-the-art (SOTA) NLP models are highly integrated with deep learning techniques. This study presents an engineering framework of medical entity recognition, relation extraction and attribute extraction.
  arXiv  Detail & Related papers  (2022-03-08T03:19:16Z)
• Statistical Dependency Guided Contrastive Learning for Multiple Labeling in Prenatal Ultrasound [56.631021151764955]
  Standard plane recognition plays an important role in prenatal ultrasound (US) screening. We build a novel multi-label learning scheme to identify multiple standard planes and corresponding anatomical structures simultaneously.
  arXiv  Detail & Related papers  (2021-08-11T06:39:26Z)
• Self-supervised Answer Retrieval on Clinical Notes [68.87777592015402]
  We introduce CAPR, a rule-based self-supervision objective for training Transformer language models for domain-specific passage matching. We apply our objective in four Transformer-based architectures: Contextual Document Vectors, Bi-, Poly- and Cross-encoders. We report that CAPR outperforms strong baselines in the retrieval of domain-specific passages and effectively generalizes across rule-based and human-labeled passages.
  arXiv  Detail & Related papers  (2021-08-02T10:42:52Z)
• Clinical Named Entity Recognition using Contextualized Token Representations [49.036805795072645]
  This paper introduces the technique of contextualized word embedding to better capture the semantic meaning of each word based on its context. We pre-train two deep contextualized language models, Clinical Embeddings from Language Model (C-ELMo) and Clinical Contextual String Embeddings (C-Flair) Explicit experiments show that our models gain dramatic improvements compared to both static word embeddings and domain-generic language models.
  arXiv  Detail & Related papers  (2021-06-23T18:12:58Z)
• An analysis of full-size Russian complexly NER labelled corpus of Internet user reviews on the drugs based on deep learning and language neural nets [94.37521840642141]
  We present the full-size Russian complexly NER-labeled corpus of Internet user reviews. A set of advanced deep learning neural networks is used to extract pharmacologically meaningful entities from Russian texts.
  arXiv  Detail & Related papers  (2021-04-30T19:46:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.