Healthcare Text Analytics: Unlocking the Evidence from Free Text, Volume VI

Text analytics encompasses a diverse array of computational techniques and methodologies aimed at extracting meaningful insights from free-form textual data. In healthcare domain, electronic health records, clinical notes, biomedical literature, patient-generated texts in social media, and veterinary medicine texts are abundant and valuable. By seamlessly integrating computational techniques with healthcare informatics, this comprehensive guide empowers users with actionable intelligence, enabling informed decision-making. "Text Analytics: Unlocking the Evidence from Free Text, Volume VI" serves as a beacon of knowledge in this dynamic landscape, providing readers with a wealth of insights, methodologies, and best practices for unlocking the evidential potential inherent in free text.The goal of this Research Topic is to address the challenge through the utilization of advanced text analytics methodologies tailored for the healthcare domain. These include analysis, information retrieval, information extraction, Named Entity Recognition (NER), text summarization, question answering, sentiment analysis, and topic modeling methods. These approaches empower both researchers and practitioners to reveal valuable insights concealed within free text, thereby enhancing patient care, clinical decision-making, and operational efficiency. Recent progress in natural language processing (NLP), machine learning and deep learning, and especially in Generative AI, like Large Language Models and chatbots, and multimodality learning, e.g. with imaging and tabular data, has notably elevated the effectiveness of text analytics within the healthcare domain. These advancements offer promising avenues for leveraging textual data to improve various aspects of healthcare delivery and management.In conjunction with the HealTAC 2026 conference, suitable themes for manuscripts we welcome include (but are not limited to):- Text analytics and learning health systems LLMs for healthcare text analytics;- Datasets for healthcare text analytics Multi-modal models for healthcare decision support;- Information extraction in healthcare text;- Ontologies and knowledge graphs for healthcare text;- Sharing resources for healthcare text analytics (data and methods);- Human-AI collaboration in healthcare NLP Real-world application of healthcare text analytics;- Processing patient-generated data (e.g. social media, health forums, diaries). Topic editors: Hang Dong, Tao Wang, Yamiko Joseph Msosa Source: https://www.frontiersin.org/research-topics/79680/healthcare-text-analytics-unlocking-the-evidence-from-free-text-volume-vi