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|<ref>{{Cite journal|last=Kim|first=J.-D.|last2=Ohta|first2=T.|last3=Tateisi|first3=Y.|last4=Tsujii|first4=J.|date=2003-07-03|title=GENIA corpus--a semantically annotated corpus for bio-textmining|url=https://academic.oup.com/bioinformatics/article/19/suppl_1/i180/227927|journal=Bioinformatics|language=en|volume=19|issue=Suppl 1|pages=i180–i182|doi=10.1093/bioinformatics/btg1023|issn=1367-4803}}</ref><ref>{{Cite web|url=http://www.geniaproject.org/|title=GENIA Project|website=www.geniaproject.org|access-date=2018-10-06}}</ref>
|<ref>{{Cite journal|last=Kim|first=J.-D.|last2=Ohta|first2=T.|last3=Tateisi|first3=Y.|last4=Tsujii|first4=J.|date=2003-07-03|title=GENIA corpus--a semantically annotated corpus for bio-textmining|url=https://academic.oup.com/bioinformatics/article/19/suppl_1/i180/227927|journal=Bioinformatics|language=en|volume=19|issue=Suppl 1|pages=i180–i182|doi=10.1093/bioinformatics/btg1023|issn=1367-4803}}</ref><ref>{{Cite web|url=http://www.geniaproject.org/|title=GENIA Project|website=www.geniaproject.org|access-date=2018-10-06}}</ref>
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|FamPlex
|Bachman ''et al.''
|Protein names and families linked to unique identifiers. Includes [[affix]] sets.
|Yes
|<ref>{{Cite journal|last=Bachman|first=John A.|last2=Gyori|first2=Benjamin M.|last3=Sorger|first3=Peter K.|date=2018-06-28|title=FamPlex: a resource for entity recognition and relationship resolution of human protein families and complexes in biomedical text mining|url=https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2211-5|journal=BMC Bioinformatics|language=En|volume=19|issue=1|doi=10.1186/s12859-018-2211-5|issn=1471-2105|pmc=PMC6022344|pmid=29954318}}</ref>
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|IEPA
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Revision as of 22:26, 6 October 2018

Biomedical text mining (also known as 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 and molecular biology domains. As a field of research, biomedical text mining incorporates ideas from natural language processing, bioinformatics, medical informatics and computational linguistics. The strategies developed through studies in this field are frequently applied to the biomedical and molecular biology literature available through services such as PubMed.

Challenges

Applying text mining approaches to biomedical text presents specific challenges 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,[1] product reviews,[2] 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.[3] 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[4][5][6] and biomedical informatics researchers.[7][8] 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).

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.[9]

Supporting clinical needs

Biomedical text mining applications developed for clinical use should ideally reflect the needs and demands of clinicians.[3] This is a concern in environments where clinical decision support is expected to be informative and accurate.

Working with other clinical systems

New text mining systems must be interoperable with existing standards, electronic medical records, and databases.[3] Methods for interfacing with clinical systems such as LOINC have been developed[10] but require extensive organizational effort to implement and maintain.[11][12]

Patient privacy

Text mining systems operating with private medical data must respect its security and ensure it is rendered anonymous where appropriate.[13][14][15]

Processes

Specific text mining 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,[16] chemical compounds and drugs,[17] and disease names[18] 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.[19]

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.[20]

Hedge cue detection

The challenge of identifying uncertain or "hedged" statements has been addressed through hedge cue detection in biomedical literature.[9]

Claim detection

Multiple researchers have developed methods to identify specific scientific claims from literature.[21][22]

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 "freely available" can be downloaded from a publicly accessible location and used without registration or prior agreement.

Biomedical Text Corpora
Corpus Name Authors or Group Contents Freely Available Citation
2006 i2b2 Deidentification and Smoking Challenge i2b2 889 de-identified medical discharge summaries annotated for patient identification and smoking status features. No [23][24]
2008 i2b2 Obesity Challenge i2b2 1,237 de-identified medical discharge summaries annotated for presence or absence of comorbidities of obesity. No [25]
2009 i2b2 Medication Challenge i2b2 1,243 de-identified medical discharge summaries annotated for names and details of medications, including dosage, mode, frequency, duration, reason, and presence in a list or narrative structure. No [26][27]
2010 i2b2 Relations Challenge i2b2 Medical discharge summaries annotated for medical problems, tests, treatments, and the relations among these concepts. Only a subset of these data records are available for research use due to IRB limitations. No [4]
2011 i2b2 Coreference Challenge i2b2 978 de-identified medical discharge summaries, progress notes, and other clinical reports annotated with concepts and coreferences. Includes the ODIE corpus. No [28]
2012 i2b2 Temporal Relations Challenge i2b2 310 de-identified medical discharge summaries annotated for events and temporal relations. No [5]
2014 i2b2 De-identification Challenge i2b2 1,304 de-identified longitudinal medical records annotated for protected health information (PHI). No [29]
2014 i2b2 Heart Disease Risk Factors Challenge i2b2 1,304 de-identified longitudinal medical records annotated for risk factors for cardiac artery disease. No [30]
AIMed Bunescu et al. 200 abstracts annotated for protein–protein interactions, as well as negative example abstracts containing no protein-protein interactions. Yes [31]
BioCreAtIvE 1 BioCreAtIvE 15,000 sentences (10,000 training and 5,000 test) annotated for protein and gene names. 1,000 full text biomedical research articles annotated with protein names and Gene Ontology terms. Yes [32]
BioCreAtIvE 2 BioCreAtIvE 15,000 sentences (10,000 training and 5,000 test, different from the first corpus) annotated for protein and gene names. 542 abstracts linked to EntrezGene identifiers. A variety of research articles annotated for features of protein–protein interactions. Yes [33]
BioInfer Pyysalo et al. 1,100 sentences from biomedical research abstracts annotated for relationships, named entities, and syntactic dependencies. No [34]
BioScope Vincze et al. 1,954 clinical reports, 9 papers, and 1,273 abstracts annotated for linguistic scope and terms denoting negation or uncertainty. Yes [35]
CRAFT Verspoor et al. 97 full-text biomedical publications annotated with linguistic structures and biological concepts Yes [36]
GENIA Corpus GENIA Project 1,999 biomedical research abstracts on the topics "human", "blood cells", and "transcription factors", annotated for parts of speech, syntax, terms, events, relations, and coreferences. Yes [37][38]
FamPlex Bachman et al. Protein names and families linked to unique identifiers. Includes affix sets. Yes [39]
IEPA Ding et al. 486 sentences from biomedical research abstracts annotated for pairs of co-occurring chemicals, including proteins. No [40]
Learning Language in Logic (LLL) Nédellec et al. 77 sentences from research articles about the bacterium Bacillus subtilis, annotated for protein–gene interactions. Yes [41]
ODIE Corpus Savova et al. 180 clinical notes annotated with 5,992 coreference pairs. No [42]
OHSUMED Hersh et al. 348,566 biomedical research abstracts and indexing information from MEDLINE, including MeSH (as of 1991). Yes [43]
PMC Open Access Subset PubMed Central More than 2 million research articles, updated weekly. Yes [44]
Yapex Franzén et al. 200 biomedical research abstracts annotated with protein names. No [45]

Applications

A flowchart of a text mining protocol.
An example of a text mining protocol used in a study of protein-protein complexes, or protein docking.[46]

Text mining applications in the biomedical field include computational approaches to assist with studies in protein docking,[47] protein interactions,[48][49] and protein-disease associations.[50]

Gene cluster identification

Methods for determining the association of gene clusters obtained by microarray experiments with the biological context provided by the corresponding literature have been developed.[51]

Protein interactions

Automatic extraction of protein interactions[52] and associations of proteins to functional concepts (e.g. gene ontology terms) has been explored.[citation needed] Even the extraction of kinetic parameters from text or the subcellular location of proteins have been addressed by information extraction and text mining technology.

Protein-disease associations

Text mining enables an unbiased evaluation of protein-disease relationships within a vast quantity of unstructured textual data.[53]

Applications of phrase mining to disease associations

A text mining study assembled a collection of 709 core extracellular matrix proteins and associated proteins based on two databases: MatrixDB (matrixdb.univ-lyon1.fr) and UniProt. This set of proteins had a manageable size and a rich body of associated information, making it a suitable for the application of text mining tools. The researchers conducted phrase-mining analysis to cross-examine individual extracellular matrix proteins across the biomedical literature concerned with six categories of cardiovascular diseases. They used a phrase-mining pipeline, Context-aware Semantic Online Analytical Processing (CaseOLAP),[54] then semantically scored all 709 proteins according to their Integrity, Popularity, and Distinctiveness using the CaseOLAP pipeline. The text mining study validated existing relationships and informed previously unrecognized biological processes in cardiovascular pathophysiology.[50]

Software tools

Search engines

Search engines designed to retrieve biomedical literature relevant to a user-provided query frequently rely upon text mining approaches. Publicly-available tools specific for research literature include PubMed search, Europe PubMed Central search, GeneView,[55] and APSE[56] Similarly, search engines and indexing systems specific for biomedical data have been developed, including DataMed[57] and OmicsDI.[58]

Some search engines, such as Essie,[59] OncoSearch,[60] PubGene,[61][62] and GoPubMed[63] were previously public but have since been discontinued, rendered obsolete, or integrated into commercial products.

Medical record analysis systems

Electronic medical records (EMRs) and electronic health records (EHRs) are collected by clinical staff in the course of diagnosis and treatment. Though these records generally include structured components with predictable formats and data types, the remainder of the reports are often free-text. Numerous complete systems and tools have been developed to analyse these free-text portions.[64] The MedLEE system was originally developed for analysis of chest radiology reports but later extended to other report topics.[65] The clinical Text Analysis and Knowledge Extraction System, or cTAKES, annotates clinical text using a dictionary of concepts.[66] The CLAMP system offers similar functionality with a user-friendly interface.[67]

See also

References

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Conferences

External links

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