Healthcare Text Analytics: Unlocking the Evidence from Free Text - Vol. IHealthcare narratives (such as clinical notes, discharge letters, nurse handover notes, imaging reports, patients posts on social media or feedback comments, etc.) have 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 still 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 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.
In conjunction with the
HealTAC 2021 conference, we welcome contributions to this Research Topic that address the variety of aspects involved in processing and using healthcare free text with the aim of improving healthcare. This Research Topic is also open to public submissions, as well as those based on talks given at the conference.
Examples of 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
Dr. Angus Roberts has received funding from Takeda Pharmaceutical Company Ltd. All other Topic Editors declare no competing interests with regards to the Research Topic subject.
Please note: Any submissions co-authored by a Topic Editor, will be handled by an independent member of the editorial board of Frontiers in Digital Health to ensure the integrity of the editorial process. The Topic Editors will not be involved in any stage of the peer-review of these manuscripts.