Calls

Call for contributions

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 2024 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