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Awesome Biomedical Information Extraction

🧫 A curated list of resources relevant to doing Biomedical Information Extraction (including BioNLP)

Here you can see meta information about this topic like the time we last updated this page, the original creator of the awesome list and a link to the original GitHub repository.

Last Update: Nov. 25, 2020, 12:07 a.m.

Thank you caufieldjh & contributors
View Topic on GitHub:
caufieldjh/awesome-bioie

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Research Overviews

An overview of how BioIE and bioinformatics workflows can be applied to questions in cardiovascular health and medicine research.

A review of clinical IE papers published as of September 2016. From Mayo Clinic group (see below).

A review of Literature Based Discovery (LBD), or the philosophy that meaningful connections may be found between seemingly unrelated scientific literature.

A review of the methods and philosophy behind mining electronic health records, including using them for adverse event detection. See Table 2 for a list of relevant papers as of mid-2017.

A 2017 review of natural language processing methods applied to information extraction in health records and social media text. An important note from this review: "One of the main challenges in the field is the availability of data that can be shared and which can be used by the community to push the development of methods based on comparable and reproducible studies".

Groups Active in the Field

Led by Dr. Guergana Savova, formerly at Mayo Clinic and the Apache cTAKES project.

The U.S. National Institutes of Health (NIH) funded 13 Centers of Excellence through their Big Data to Knowledge (BD2K) program, several of which developed tools and resources for BioIE.

Based at University of California, Los Angeles (UCLA). Led by Dr. Peipei Ping.

Based an University of Illinois at Urbana-Champaign (UIUC). Led by Dr. Jiawei Han.

Based at Stanford. Led by Dr. Scott Delp.

Based at Brown University and directed by Dr. Neil Sarkar, whose research group works on topics in clinical NLP and IE.

Develops improvements to biomedical literature search and curation (e.g., through PubMed), led by Dr. Zhiyong Lu.

Based at the Novo Nordisk Foundation Center for Protein Research at the University of Copenhagen, Denmark.

Based at the University of Manchester and led by Prof. Sophia Ananiadou, NaCTeM is concerned with text mining in general but has a particular focus on biomedical applications.

Several groups at Mayo Clinic have made major contributions to BioIE (for example, the Apache cTAKES platform) over the past 20 years.

A joint effort between groups at Oregon State University, Oregon Health & Science University, Lawrence Berkeley National Lab, The Jackson Laboratory, and several others, seeking to "integrate biological information using semantics, and present it in a novel way, leveraging phenotypes to bridge the knowledge gap".

Based at the University of Turku and concerned with NLP in general with a focus on BioNLP and clinical applications.

Based in the University of Texas Health Science Center at Houston, School of Biomedical Informatics and led by Dr. Hua Xu.

Based at Virginia Commonwealth University and led by Dr. Bridget McInnes.

Group led by Dr. Isaac Kohane at Harvard Medical School's Department of Biomedical Informatics (Dr. Kohane is also a steward of the n2c2 (formerly i2b2) datasets - see Datasets below).

Organizations

Many—but certainly not all—individuals studying biomedical informatics are members of the American Medical Informatics Association. AMIA publishes a journal, JAMIA (see below).

The International Medical Informatics Association. Publishes the IMIA Yearbook of Medical Informatics.

Journals

Its subtitle is "The Journal of Biological Databases and Curation". Open access.

NAR

Nucleic Acids Research. Has a broad biomolecular focus but is particularly notable for its annual database issue.

The Journal of the American Medical Informatics Association. Concerns "articles in the areas of clinical care, clinical research, translational science, implementation science, imaging, education, consumer health, public health, and policy".

JBI

The Journal of Biomedical Informatics. Not open access by default, though it does have an open-access "X" version.

An open-access Springer Nature journal publishing "descriptions of scientifically valuable datasets, and research that advances the sharing and reuse of scientific data".

Conferences and Other Events

The ACM Conference on Bioinformatics, Computational Biology, and Health Informatics. Held annually since 2010.

The IEEE International Conference on Bioinformatics and Biomedicine.

The International Conference on Intelligent Systems for Molecular Biology is an annual conference hosted by the International Society for Computational Biology since 1993. Much of its focus has concerned bioinformatics and computational biology without an explicit clinical focus, though it has included an increasing amount of text mining content (e.g., the 2019 meeting included a full-day special session on Text Mining for Biology and Healthcare). The meeting is combined with that of the European Conference on Computational Biology (ECCB) on odd-numbered years.

PSB

The Pacific Symposium on Biocomputing.

Challenges

Challenges on biomedical semantic indexing and question answering. Challenges and workshops held annually since 2013.

These workshops have been organized since 2004, with BioCreative VI happening February 2017 and the BioCreative/OHNLP Challenge held in 2018. See Datasets below.

Tasks and evaluations in computational semantic analysis. Tasks vary by year but frequently cover scientific and/or biomedical language, e.g. the SemEval-2019 Task 12 on Toponym Resolution in Scientific Papers.

Challenges for encouraging "development of software technologies to automatically extract a large variety of knowledge from eHealth documents written in the Spanish Language". Previously held as part of TASS, an annual workshop for semantic analysis in Spanish.

Held along with several other more bioinformatics-focused challenges, this challenge opened in October 2019 and focuses on using electronic health record data to predict patient mortality. Uses a synthetic data set rather than real EHR contents.

Guides

A brief introduction to bio-text mining from Cohen and Hunter. More than ten years old but still quite relevant. See also an earlier paper by the same authors.

A (non-free) volume of Methods in Molecular Biology from 2014. Chapters covers introductory principles in text mining, applications in the biological sciences, and potential for use in clinical or medical safety scenarios.

Video Lectures and Online Courses

About three hours worth of video lectures on working with medical data of various types and structures, including text and image data. Appears fairly high-level and intended for beginners.

This training workshop happenened in 2013 but the slides are still online.

Code Libraries

A smorgasbord architecture for coreference resolution in biomedical text

7
1
7m
NOASSERTION

Medical Text Mining and Information Extraction with spaCy

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65
114d
GPL-3.0

A full spaCy pipeline and models for scientific/biomedical documents.

670
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4m
Apache-2.0

talk with NCBI entrez using R

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33
1y 23d
NOASSERTION

paper](http://dx.doi.org/10.1093/bioinformatics/btp163) - code - Python tools primarily intended for bioinformatics and computational molecular biology purposes, but also a convenient way to obtain data, including documents/abstracts from PubMed (see Chapter 9 of the documentation).

paper](https://arxiv.org/abs/2003.01271) - code - a Python package and model (for use with spaCy) for doing NER with medication-related concepts.

Repos for Specific Datasets

MIMIC Code Repository: Code shared by the research community for the MIMIC-III database

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MIT

Tools, Platforms, and Services

Public release of the DeepPhe analytic software

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6
6m
NOASSERTION

A framework for keeping biomedical text mining result up-to-date

30
6
4m
MIT

Surfacing Semantic Data from Clinical Notes in Electronic Health Records for Tailored Care, Trial Recruitment and Clinical Research

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11
5m
Apache-2.0

Framework for information extraction from tables

25
10
1y 7m
Unknown

Apache clinical Text Analysis and Knowledge Extraction System (cTAKES) is an open-source natural language processing system for information extraction from electronic medical record clinical free-text.

paper](https://academic.oup.com/jamia/article/25/3/331/4657212) - A natural language processing toolkit intended for use with the text in clinical reports. Check out their live demo first to see what it does. Usable at no cost for academic research.

paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3810844/) - A method for disease normalization, i.e., linking mentions of disease names and acronyms to unique concept identifiers. Downloadable version includes the NCBI Disease Corpus and BC5CDR (see Annotated Text Data below).

paper](https://academic.oup.com/nar/article/47/W1/W587/5494727) - A web platform that identifies five different types of biomedical concepts in PubMed articles and PubMed Central full texts. The full annotation sets are downloadable (see Annotated Text Data below).

paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5018376/) - Performs concept normalization (see also DNorm above). Can be trained for specific concept types and can perform NER independent of other normalization functions.

Annotation Tools

Anafora is a web-based raw text annotation tool

202
50
5m
Unknown

paper](https://www.aclweb.org/anthology/E12-2021/) - code - The brat rapid annotation tool. Supports producing text annotations visually, through the browser. Not subject specific; appropriate for many annotation projects. Visualization is based on that of the stav tool.

Word Embeddings

paper](http://bioasq.lip6.fr/info/BioASQword2vec/) - Qord embeddings derived from biomedical text (>10 million PubMed abstracts) using the popular word2vec tool.

paper](https://www.nature.com/articles/s41597-019-0055-0) - code - Word embeddings derived from biomedical text (>27 million PubMed titles and abstracts), including subword embedding model based on MeSH.

Language Models

BioBERT: a pre-trained biomedical language representation model for biomedical text mining

395
50
5m
Unknown

repository for Publicly Available Clinical BERT Embeddings

255
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92d
MIT

ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission (CHIL 2020 Workshop)

146
36
4m
Unknown

A very simple framework for state-of-the-art Natural Language Processing (NLP)

9.59K
1.38K
2d
n/a

A BERT model for scientific text.

704
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5m
Apache-2.0

BlueBERT, pre-trained on PubMed abstracts and clinical notes (MIMIC-III).

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24
5m
n/a

paper - A BERT model trained from scratch on PubMed, with versions trained on abstracts+full texts and on abstracts alone.

Biomedical Text Sources

paper](https://dl.acm.org/citation.cfm?id=188557) - 348,566 MEDLINE entries (title and sometimes abstract) from between 1987 and 1991. Includes MeSH labels. Primarily of historical significance.

A set of PubMed Central articles usable under licenses other than traditional copyright, though the exact licenses vary by publication and source. Articles are available as PDF and XML.

A corpus of scholarly manuscripts concerning COVID-19. Articles are primarily from PubMed Central and preprint servers, though the set also includes metadata on papers without full-text availability.

Annotated Text Data

paper](https://www.nature.com/articles/sdata20181) - A pilot dataset containing standardised information, and annotations of occurence in text, about ~5,000 known adverse reactions for 200 FDA-approved drugs.

paper](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-6-S1-S1) - 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.

paper](https://academic.oup.com/database/article/doi/10.1093/database/baw068/2630414) - 1,500 articles (title and abstract) published in 2014 or later, annotated for 4,409 chemicals, 5,818 diseases and 3116 chemical–disease interactions. Requires registration.

paper](https://pdfs.semanticscholar.org/eed7/81f498b563df5a9e8a241c67d63dd1d92ad5.pdf) - >2,400 articles annotated with chemical-protein interactions of a variety of relation types. Requires registration.

The Department of Biomedical Informatics (DBMI) at Harvard Medical School manages data for the National NLP Clinical Challenges and the Informatics for Integrating Biology and the Bedside challenges running since 2006. They require registration before access and use. Datasets include a variety of topics. See the list of data challenges for individual descriptions.

paper](https://www.sciencedirect.com/science/article/pii/S1532046413001974) - A corpus of 793 biomedical abstracts annotated with names of diseases and related concepts from MeSH and OMIM.

paper](https://academic.oup.com/nar/article/47/W1/W587/5494727) - A web platform that identifies five different types of biomedical concepts in PubMed articles and PubMed Central full texts. The full annotation sets are downloadable (see Annotated Text Data below).

paper](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-12-223) - 203 ambiguous words and 37,888 automatically extracted instances of their use in biomedical research publications. Requires UTS account.

also known as CQC or the Iowa collection, these are several thousand questions posed by physicians during office visits along with the associated answers.

data from six shared tasks, though some may not be easily accessible; try the CG task set (BioNLP2013CG) for extensive entity and event annotations.

paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2586758/) - a corpus of sentences from medical and biological documents, annotated for negation, speculation, and linguistic scope.

Protein-protein Interaction Annotated Corpora

paper](https://www.ncbi.nlm.nih.gov/pubmed/15811782) - 225 MEDLINE abstracts annotated for PPI.

paper](https://academic.oup.com/database/article/doi/10.1093/database/baw147/2884890) - 120 full text articles annotated for PPI and genetic interactions. Used in the BioCreative V BioC task.

paper](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-8-50) - 1,100 sentences from biomedical research abstracts annotated for relationships (including PPI), named entities, and syntactic dependencies. Additional information and download links are here.

paper](https://academic.oup.com/bioinformatics/article/23/3/365/236564) - 50 scientific abstracts referenced by the Human Protein Reference Database, annotated for PPI.

paper](http://psb.stanford.edu/psb-online/proceedings/psb02/abstracts/p326.html) - 486 sentences from biomedical research abstracts annotated for pairs of co-occurring chemicals, including proteins (hence, PPI annotations).

LLL

paper](https://www.semanticscholar.org/paper/Learning-Language-in-Logic-Genic-Interaction-Nedellec/0863a9d71955341b7e1a6a6877d44d4f0bb22671) - 77 sentences from research articles about the bacterium Bacillus subtilis, annotated for protein–gene interactions (so, fairly close to PPI annotations). Additional information is here.

Other Datasets

paper](https://www.nature.com/articles/sdata2018273) - A database of prevalence and co-occurrence frequencies of conditions, drugs, procedures, and patient demographics extracted from electronic health records. Does not include original record text.

paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6323936/) - A database of manually curated associations between chemicals, gene products, phenotypes, diseases, and environmental exposures. Useful for assembling ontologies of the related concepts, such as types of chemicals.

paper](https://www.nature.com/articles/sdata201635) - Deidentified health data from ~60,000 intensive care unit admissions. Requires completion of an online training course (CITI training) and acceptance of a data use agreement prior to use.

The MIMIC Chest X-Ray database. Contains more than 377,000 radiographic images and accompanying free-text radiology reports. As with MIMIC-III, requires acceptance of a data use agreement.

reference manual](https://www.ncbi.nlm.nih.gov/books/NBK9676/) - A large and comprehensive collection of biomedical terminology and identifiers, as well as accompanying tools and scripts. Depending on your purposes, the single file MRCONSO.RRF may be sufficient, as this file contains unique identifiers and names for all concepts in the UMLS Metathesaurus. See also the Ontologies and Controlled Vocabularies section below.

An update to MIMIC-III's multimodal patient data, now covering more recent years of admissions, plus a new data structure, emergency department records, and links to MIMIC-CXR images.

Ontologies and Controlled Vocabularies

paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4383880/) - An ontology of human diseases. Has cross-links to MeSH, ICD, NCI Thesaurus, SNOMED, and OMIM. Public domain. Available on GitHub and on the OBO Foundry.

paper](https://academic.oup.com/jamia/article/18/4/441/734170) - Normalized names for clinical drugs and drug packs, with combined ingredients, strengths, and form, and assigned types from the Semantic Network (see below). Released monthly.

paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2247735/) - A general English lexicon that includes many biomedical terms. Updated yearly since 1994 and still updated as of 2019. Part of UMLS but does not require UTS account to download.

paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC308795/) - Mappings between >3.8 million concepts, 14 million concept names, and >200 sources of biomedical vocabulary and identifiers. It's big. It may help to prepare a subset of the Metathesaurus with the MetamorphoSys installation tool but we're still talking about ~30 Gb of disk space required for the 2019 release. See the manual here. Requires UTS account.

paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2447396/) - Lists of 133 semantic types and 54 semantic relationships covering biomedical concepts and vocabulary. Is the Metathesaurus too complex for your needs? Try this. Does not require UTS account to download.

Data Models

Definition and DDLs for the OMOP Common Data Model (CDM)

470
284
99d
Apache-2.0

code](https://github.com/biolink/biolink-model) - A data model of biological entities. Provided as a YAML file.

paper](https://academic.oup.com/nar/article/47/W1/W225/5498754) - An architecture for biomedical data analysis, integration, and visualization. Conceptually based on the visual modeling language UML.