The Dutch Clinical NLP Workshop

Call for Abstracts

Call for Abstracts

The growing adoption of electronic health records has led to a significant increase in the availability of clinical text. This text can be leveraged by natural language processing (NLP) systems to advance medical research and improve healthcare services. Recent years have also seen major advances in NLP systems available to hospitals.

At the DCNLP 2024, we welcome submissions encompassing a broad spectrum of topics related to clinical NLP. We encourage presentations discussing possibilities to share datasets, models, and tools for the development of Dutch clinical NLP. The focus is on improving NLP technology to enable clinical applications, and specifically, information extraction and modeling of narrative provider notes from electronic health records, patient encounter transcripts, and other clinical narratives. In practice, privacy and legal regulations typically prohibit the open sharing and combination of electronic health records, but it is frequently possible to share de-identified texts, NLP tools, and intermediate results. We hope that sharing will encourage cooperation among Dutch-speaking countries, enhancing research in Clinical NLP and ensuring the safe deployment of models in these regions.

Relevant topics include, but are not limited to:

  • Modeling clinical text in standard NLP tasks (tagging, chunking, parsing, entity identification, entity linking/normalization, relation extraction, coreference, summarization, etc.)
  • Data sets with clinical texts
  • Open source tools for Clinical NLP
  • Information extraction from clinical text
  • Information retrieval for clinical text
  • Adapting standard NLP tools for clinical text
  • De-identification and ways to preserve privacy in clinical data
  • Using medical terminologies and ontologies
  • Annotation schemes and annotation methodology for clinical data
  • Evaluation methods for the clinical domain
  • Text-based clinical prediction models
  • Speech recognition for clinical text
  • Generation of clinical notes: summarization, image-to-text, generation of notes from clinical conversations, etc.
  • Domain adaptation and transfer learning techniques for clinical data
  • Bias and fairness in clinical text


We invite researchers, clinicians, and industry professionals to submit abstracts for presentation at the 3rd Dutch Clinical NLP Workshop, focused on applications of NLP in healthcare. This workshop aims to bring together experts and enthusiasts in the field to share innovative research, discuss challenges, and explore the potential of NLP in improving clinical outcomes.

Deadline: May 1, 2024



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