Healthcare narrative (such as clinical notes, discharge letters, nurse handover notes, imaging reports, patients posts on social media or feedback comments, etc.) has been used as a key communication stream that contains the majority of actionable and contextualised healthcare data, but which – despite being increasingly available in a digital form – is not routinely analysed, and is rarely integrated with other healthcare data on a large-scale. There are many barriers and challenges in processing healthcare free text, including, for example, the variability and implicit nature of language expressions, and difficulties in sharing training and evaluation data. On the other hand, recent years have witnessed increasing opportunities to process free text, with a number of success stories that have demonstrated the feasibility of using advanced Natural Language Processing to unlock evidence contained in free text to support clinical care, patient self-management, epidemiological research and audit.

Topics

HealTAC 2025 invites contributions that address any aspect of healthcare text analytics, including (but not limited to) the following topics:

  • (Large) language models for healthcare text analytics
  • Machine-learning approaches to healthcare text analytics
  • Transfer learning for healthcare text analytics
  • Speech analytics for healthcare applications
  • Processing clinical literature and trial reports
  • Multi-modal models for healthcare decision support
  • Text analytics and learning health systems
  • Healthcare ontologies and coding of healthcare text
  • Explainable models for healthcare NLP
  • Real-time processing of healthcare free text
  • Real-world application of healthcare text analytics
  • Scalable and secure healthcare NLP infrastructures
  • Text mining for veterinary medicine
  • Privacy-preserving healthcare analytics
  • Datasets for healthcare text analytics
  • Reproducibility in the healthcare text analytics
  • Evaluation and assessment of text analytics methods
  • Sharing resources for healthcare text analytics (data and methods)
  • Information extraction: identification of clinical variables and their values in free-text
  • Processing patient-generated data (e.g. social media, health forums, diaries)
  • Implementation of healthcare text analytics in practice: public engagement and governance

Contribution types

Extended abstracts

TBC

PhD and fellowship projects

TBC

Panel discussions

TBC

Software demo sessions

TBC

Submission Template

TBC

All deadlines are 11:59PM UTC-12:00 (“anywhere on Earth”).

TBC