Now that electronic health records are commonly used, the availability of clinical texts is growing. This workshop discusses the automatic analysis of textual clinical health data to advance medical research and improve healthcare related services. We especially encourage presentations discussing possibilities to share clinical texts, models and tools for clinical natural language processing (NLP). In practice, privacy- and legal regulations prevent the free sharing and combination of electronic health records themselves, but de-identified texts, NLP tools and intermediate results may be shared. We hope that sharing will promote cooperation within the Dutch-speaking countries, as well as advance the research in Clinical NLP in those countries. Relevant topics include, but are not limited to:
- 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
We solicit short presentations (15 to 20 minutes) from researchers covering recent work, including work in progress and work that was recently published at journals and/or conferences in the field or made available via data and software sharing platforms like Zenodo or Github. Please email the title and abstract of your presentation before 12 October 2021.
More information at: https://clinical-nlp.cs.ru.nl