An overview of MetaMap: historical perspective and recent advances

By Alan R. Aronson, François-Michel Lang. J. Am. Medical Informatics Assoc., 2010

1,612 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a4fba68413d64f48b4223c116cf57c4197bafb79

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Citations in the corpus

Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives (2018, neutral): “Examples of systems that employ such databases and extract concepts from text are the KnowledgeMap Concept Identifier (KMCI) [44], MetaMap [45], Medlee [46], MedEx [47], and the cTAKES.”
Development of phenotype algorithms using electronic medical records and incorporating natural language processing (2015, methodology): “21 30-32 Finally, we note that the general phenotyping method presented in this article were successfully applied to a range of defined diseases and conditions but has not been extensively tested on outcomes such as drug response or adverse events.”
PadChest: A large chest x-ray image dataset with multi-label annotated reports (2019, methodology): “In ChestX-Ray-8, [4] used MetaMap [21], DNorm [22] and custom negation rules applied to a syntactic parser in order to label the presence or otherwise of 8 entities, which was further expanded to 14 entities in 125,000 images (Chest-XRay14), also by applying analogous methods.”
Clinical Information Extraction Applications: A Literature Review (2017, methodology): “In two studies [27,70], researchers used GATE- and MetaMap-extracted biopsychosocial concepts from the primary care records of patients and studied their relationship to inappropriate use of ED visits.”
A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries (2011, positive): “have also been introduced into the community. Most of clinical NLP systems can extract various types of named entities from clinical text and link them to concepts in the Unified Medical Language System (UMLS), such as MetaMap”
Clinically Accurate Chest X-Ray Report Generation (2019, methodology): “Radiologists are trained to simultaneously identify various radiological ﬁndings (e.g., diseases), according to the details of the radiograph and the patient’s clinical history, then summarize these ﬁndings and their overall impression in reports for clinical commu-2 2015), Chest-XRay8 (Wang et al.,”
Supporting Information Retrieval from Electronic Health Records: A Report of University of Michigan's Nine-Year Experience in Developing and Using the Electronic Medical Record Search Engine (EMERSE) (2015, neutral): “This experimental extension also incorporates MetaMap, NLM’s named entity recognition engine [45], to enable the use of additional text features such as term frequency, inverse document frequency, and document length penalization to improve the relevance of document ranking of the search results tha”
Named Entity Recognition in Chinese Clinical Text Using Deep Neural Network (2015, methodology): “at Columbia University, the MetaMap system [4, 5] - a general biomedical NLP system developed by Aronson et al.”
Visualizing a field of research: A methodology of systematic scientometric reviews (2019, background): “Search systems have utilized additional resources such as WordNet [43] and domain ontologies [44, 45] to enhance users’ original terms with their synonyms and/or closely related concepts.”
MalaCards: an amalgamated human disease compendium with diverse clinical and genetic annotation and structured search (2016, neutral): “One of these sources, Unified Medical Language System (UMLS), is associated with a different mapping algorithm, the MetaMap program (4).”
NLM’s System Description for the Fourth i2b2/VA Challenge (2010, methodology): “The noun phrases identified by MetaMap were post-processed with an ad-hoc set of rules to (1) remove leading assertion modifiers, (2) prune prepositional phrases unless they indicate a body part or location, (3) combine phrases connected by conjunctions and other syntax that denote lists, and (4) co”
An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition (2015, methodology): “In [45], the authors follow two different approaches (participating systems mc1-5): (a) one that relies in the results provided by the MetaMap tool described in [46], and, (b) one that is based on the search engine Indri [47].”