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 Text Analytics: Unlocking the Evidence from Free Text - Vol. IV
In conjunction with the HealTAC 2024 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.
Text analytics is the cornerstone of extracting insights from unstructured text. It encompasses a diverse array of computational techniques and methodologies aimed at extracting meaningful insights from free-form textual data. In healthcare, this data from electronic health records, clinical notes, and biomedical literature is 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 V" 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 focus 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, keyword extraction, Named Entity Recognition (NER), text summarization, sentiment analysis, topic modeling, and information retrieval 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 machine learning, natural language processing (NLP), and deep learning 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.
“Healthcare Text Analytics: Unlocking the Evidence from Free Text, Volume V” seeks contributions exploring recent advancements, methodologies, and applications of text analytics in healthcare, including the utilization of large language models (LLMs). Authors are encouraged to submit original research, reviews, case studies, perspectives, and methodological papers focusing on analysis techniques, keyword extraction, Named Entity Recognition (NER), text summarization, and the applications of LLMs in healthcare. Emphasis is placed on recent advances in machine learning and natural language processing (NLP). This Research Topic aims to foster interdisciplinary collaboration and contribute to the enhancement of text analytics methodologies and their practical application in healthcare services and industries. Submissions should adhere to formatting guidelines and ethical standards.
Healthcare Text Analytics: Unlocking the Evidence from Free Text - Vol. II
Healthcare Text Analytics: Unlocking the Evidence from Free Text - Vol. III
Healthcare Text Analytics: Unlocking the Evidence from Free Text - Vol. IV
In conjunction with the HealTAC 2024 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.
Text analytics is the cornerstone of extracting insights from unstructured text. It encompasses a diverse array of computational techniques and methodologies aimed at extracting meaningful insights from free-form textual data. In healthcare, this data from electronic health records, clinical notes, and biomedical literature is 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 V" 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 focus 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, keyword extraction, Named Entity Recognition (NER), text summarization, sentiment analysis, topic modeling, and information retrieval 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 machine learning, natural language processing (NLP), and deep learning 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.
“Healthcare Text Analytics: Unlocking the Evidence from Free Text, Volume V” seeks contributions exploring recent advancements, methodologies, and applications of text analytics in healthcare, including the utilization of large language models (LLMs). Authors are encouraged to submit original research, reviews, case studies, perspectives, and methodological papers focusing on analysis techniques, keyword extraction, Named Entity Recognition (NER), text summarization, and the applications of LLMs in healthcare. Emphasis is placed on recent advances in machine learning and natural language processing (NLP). This Research Topic aims to foster interdisciplinary collaboration and contribute to the enhancement of text analytics methodologies and their practical application in healthcare services and industries. Submissions should adhere to formatting guidelines and ethical standards.
Keywords: text analytics, natural language processing, health informatics, medical informatics, text mining
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.
