Biomedical text mining (including biomedical natural language processing or BioNLP) refers to the methods and study of how text mining may be applied to texts and literature of the biomedical domain. As a field of research, biomedical text mining incorporates ideas from natural language processing, bioinformatics, medical informatics and computational linguistics. The strategies in this field have been applied to the biomedical literature available through services such as PubMed.

In recent years, the scientific literature has shifted to electronic publishing but the volume of information available can be overwhelming. This revolution of publishing has caused a high demand for text mining techniques. Text mining offers information retrieval (IR) and entity recognition (ER). IR allows the retrieval of relevant papers according to the topic of interest, e.g. through PubMed. ER is practiced when certain biological terms are recognized (e.g. proteins or genes) for further processing.

Considerations Applying text mining approaches to biomedical text requires specific considerations common to the domain.

Availability of annotated text data

Large annotated corpora used in the development and training of general purpose text mining methods (e.g., sets of movie dialogue, product reviews, or Wikipedia article text) are not specific for biomedical language. While they may provide evidence of general text properties such as parts of speech, they rarely contain concepts of interest to biologists or clinicians. Development of new methods to identify features specific to biomedical documents therefore requires assembly of specialized corpora. Resources designed to aid in building new biomedical text mining methods have been developed through the Informatics for Integrating Biology and the Bedside (i2b2) challenges and biomedical informatics researchers. Text mining researchers frequently combine these corpora with the controlled vocabularies and ontologies available through the National Library of Medicine's Unified Medical Language System (UMLS) and Medical Subject Headings (MeSH).

Machine learning-based methods often require very large data sets as training data to build useful models. Manual annotation of large text corpora is not realistically possible. Training data may therefore be products of weak supervision or purely statistical methods.

Data structure variation Like other text documents, biomedical documents contain unstructured data. Research publications follow different formats, contain different types of information, and are interspersed with figures, tables, and other non-text content. Both unstructured text and semi-structured document elements, such as tables, may contain important information that should be text mined. Clinical documents may vary in structure and language between departments and locations. Other types of biomedical text, such as drug labels, may follow general structural guidelines but lack further details.

Uncertainty Biomedical literature contains statements about observations that may not be statements of fact. This text may express uncertainty or skepticism about claims. Without specific adaptations, text mining approaches designed to identify claims within text may mis-characterize these "hedged" statements as facts.

Supporting clinical needs Biomedical text mining applications developed for clinical use should ideally reflect the needs and demands of clinicians. This is a concern in environments where clinical decision support is expected to be informative and accurate. A comprehensive overview of the development and uptake of NLP methods applied to free-text clinical notes related to chronic diseases is presented in.

Interoperability with clinical systems New text mining systems must work with existing standards, electronic medical records, and databases. Methods for interfacing with clinical systems such as LOINC have been developed but require extensive organizational effort to implement and maintain.

Patient privacy Text mining systems operating with private medical data must respect its security and ensure it is rendered anonymous where appropriate.

Processes Specific sub tasks are of particular concern when processing biomedical text.

Named entity recognition Developments in biomedical text mining have incorporated identification of biological entities with named entity recognition, or NER. Names and identifiers for biomolecules such as proteins and genes, chemical compounds and drugs, and disease names have all been used as entities. Most entity recognition methods are supported by pre-defined linguistic features or vocabularies, though methods incorporating deep learning and word embeddings have also been successful at biomedical NER.

Document classification and clustering Biomedical documents may be classified or clustered based on their contents and topics. In classification, document categories are specified manually, while in clustering, documents form algorithm-dependent, distinct groups. These two tasks are representative of supervised and unsupervised methods, respectively, yet the goal of both is to produce subsets of documents based on their distinguishing features. Methods for biomedical document clustering have relied upon k-means clustering.

Relationship discovery Biomedical documents describe connections between concepts, whether they are interactions between biomolecules, events occurring subsequently over time (i.e., temporal relationships), or causal relationships. Text mining methods may perform relation discovery to identify these connections, often in concert with named entity recognition.

Hedge cue detection The challenge of identifying uncertain or "hedged" statements has been addressed through hedge cue detection in biomedical literature.

Claim detection Multiple researchers have developed methods to identify specific scientific claims from literature. In practice, this process involves both isolating phrases and sentences denoting the core arguments made by the authors of a document (a process known as argument mining, employing tools used in fields such as political science) and comparing claims to find potential contradictions between them.

Information extraction Information extraction, or IE, is the process of automatically identifying structured information from unstructured or partially structured text. IE processes can involve several or all of the above activities, including named entity recognition, relationship discovery, and document classification, with the overall goal of translating text to a more structured form, such as the contents of a template or knowledge base. In the biomedical domain, IE is used to generate links between concepts described in text, such as gene A inhibits gene B and gene C is involved in disease G. Biomedical knowledge bases containing this type of information are generally products of extensive manual curation, so replacement of manual efforts with automated methods remains a compelling area of research.

Information retrieval and question answering Biomedical text mining supports applications for identifying documents and concepts matching search queries. Search engines such as PubMed search allow users to query literature databases with words or phrases present in document contents, metadata, or indices such as MeSH. Similar approaches may be used for medical literature retrieval. For more fine-grained results, some applications permit users to search with natural language queries and identify specific biomedical relationships.

On 16 March 2020, the National Library of Medicine and others launched the COVID-19 Open Research Dataset (CORD-19) to enable text mining of the current literature on the novel virus. The dataset is hosted by the Semantic Scholar projectSemantics Scholar. (2020) "Cut through the clutter:[Open Access] Download the Coronavirus Open Research Dataset". Semantics Scholar website Retrieved 30 March 2020 of the Allen Institute for AI.Brennan, Patti. (24 March 2020). "Blog:How Does a Library Respond to a Global Health Crisis?". National Library of Medicine website Retrieved 30 March 2020. Other participants include Google, Microsoft Research, the Center for Security and Emerging Technology, and the Chan Zuckerberg Initiative.

Resources

Corpora The following table lists a selection of biomedical text corpora and their contents. These items include annotated corpora, sources of biomedical research literature, and resources frequently used as vocabulary and/or ontology references, such as MeSH. Items marked "Yes" under "Freely Available" can be downloaded from a publicly accessible location.