Biomedical Text Mining and Natural Language Processing
A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Processes".
Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 688
Special Issue Editors
Interests: biomedical text mining; natural language processing; deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning; semantic web; sentiment analysis; text mining; knowledge graphs
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the rapid development of biomedical research, a large quantity of biomedical text data is available in the biomedical domain. These biomedical texts, such as biomedical literature, clinical notes and medical guidelines, have become an important biomedical domain resource and provide a rich source of knowledge for biomedical research. However, the large size of the body of biomedical text and its rapid growth (e.g., >3000 articles are published in biomedical journals every day) make document search and information access a demanding task.
In recent years, biomedical natural language processing (NLP) or text mining with the goal of developing text mining, NLP, and machine learning techniques for various biomedical applications has received considerable attention and has seen great progress. For example, LitCovid (https://www.ncbi.nlm.nih.gov/research/coronavirus/), a curated literature hub for tracking up-to-date scientific information about COVID-19, automatically collects the COVID-19 articles and further categorizes them by research topic. Despite this success, many challenges remain to be solved in the field.
This Special Issue aims to bring together NLP researchers and experts in the biomedical field to advance the current state-of-the-art and share insights and challenges. The goal is to develop computational methods and software tools for analyzing and better understanding unstructured biomedical text data towards accelerated knowledge discovery and improving health.
Topics of interest include, but are not limited to:
- Biomedical text classification;
- Biomedical information retrieval;
- Biomedical named entity recognition and normalization (linking);
- Biomedical relation and event extractions;
- Biomedical literature-based discovery;
- Biomedical text summarization;
- Biomedical question answering;
- Pre-trained language models for biomedical NLP;
- Biomedical machine translation;
- BioNLP applications;
- BioNLP resources and evaluation.
Dr. Ling Luo
Prof. Dr. Diego Reforgiato Recupero
Guest Editors
Manuscript Submission Information
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Keywords
- biomedical informatics
- natural language processing
- text mining
- machine learning
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