About this Research Topic
Healthcare Text Analytics: Unlocking the Evidence from Free Text - Vol. I
Healthcare Text Analytics: Unlocking the Evidence from Free Text - Vol. II
Healthcare Text Analytics: Unlocking the Evidence from Free Text - Vol. III
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. 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 and language models. 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 (NLP) to unlock evidence contained in free text to support clinical care, patient self-management, epidemiological research and audit.
In conjunction with the HealTAC 2023 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
Healthcare Text Analytics: Unlocking the Evidence from Free Text - Vol. II
Healthcare Text Analytics: Unlocking the Evidence from Free Text - Vol. III
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. 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 and language models. 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 (NLP) to unlock evidence contained in free text to support clinical care, patient self-management, epidemiological research and audit.
In conjunction with the HealTAC 2023 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
Keywords: Text Analytics, Natural Language Processing, Health Informatics, Text Mining, Medical Informatics
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
