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 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. The 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 needs and 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.
HealTAC 2021 will bring the academic, clinical, industrial and patient communities together to discuss the current state of the art in processing healthcare free text and share experience, results and challenges. We invite various types of contributions, including long and short papers, posters, PhD student papers, demos, tutorial and panels, that address the variety of aspects involved in processing and using healthcare free text. The submissions page provides details – submissions are welcome from all researchers interested in this area (including international).
Following the conference, there is an open call to submit a journal length paper for further peer review and publication in Frontiers in Digital Health. In the past, post-conference special issues have appeared in Frontiers in Digital Health and Journal of Biomedical Semantics.
Topics include but are not limited to:
- Natural language processing of healthcare text
- Speech analytics for healthcare applications
- Information extraction: identification of clinical variables and their values in free-text
- Medical ontologies and coding of healthcare text
- Machine-learning approaches to healthcare text analytics
- Transfer learning for healthcare text analytics
- Processing patient-generated data (e.g. social media, health forums, diaries)
- Processing clinical literature and trial reports
- Integration of structured and unstructured resources for health applications
- Text analytics and learning health systems
- Explainable models for healthcare NLP
- Real-time processing of healthcare free text
- Real-world application of text analytics
- Scalable and secure healthcare NLP infrastructures
- Implementation of healthcare text analytics in practice: public engagement and trust
- Sharing resources for healthcare text analytics (data and methods)
- Reproducibility in the healthcare text analytics
- Evaluation and assessment of text analytics methods
- Text mining for veterinary medicine