Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program

By Alan R. Aronson. 2001

2,266 citations recorded by the source corpus.

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Citations in the corpus

Recurrent Neural Networks for Classifying Relations in Clinical Notes (2017, neutral): “, from cTakes [19], MetaMap [20], and GeniaTagger [54]) to recognize modifiers of head words in phrases [13] to address the inconsistent annotation issue.”
Predictive data mining in clinical medicine: Current issues and guidelines (2008, methodology): “An exception to this but which lies beyond the scope of this review is medical text mining where, for instance, UMLS has been used to relate medical concepts and abstracts of papers cited in Medline [38,39].”
Drug drug interaction extraction from biomedical literature using syntax convolutional neural network (2016, methodology): “Finally, all these words’ syntax word embeddings are concatenated to generate the context feature vectors ( ContF 2 R n 4 where n 4 is the dimension of the context feature vector).”
Portability of an algorithm to identify rheumatoid arthritis in electronic health records (2012, methodology): “Currently, there is a variety of NLP tools available to extract information from free text in EHR, including the medical language extraction and encoding system,(18) the KnowledgeMap Concept Identifier (KMCI),(19) the clinical Text and Knowledge Extraction System (cTAKES),(20) the Health Information”
FedNER: Medical Named Entity Recognition with Federated Learning (2020, methodology): “Other rule-based approaches involve some domain-speciﬁc knowledge bases or tools, such as MetaMap (Aronson, 2001) and UMLS (Odisho* et al., 2019).”
PREDICT: a method for inferring novel drug indications with application to personalized medicine (2011, unknown): “We began by mapping OMIM disease names to UMLS concepts, exploiting the integrated curated mapping between OMIM and UMLS concepts and augmented the mapping by using the MetaMap tool (Aronson, 2001).”
Automated encoding of clinical documents based on natural language processing. (2004, background): “MetaMap 6,20 can also be used to index text or to map terms from one vocabulary (including natural language) to another.”
Molecular Systems Biology Peer Review Process File Indi: a Computational Framework for Inferring Drug Interactions and Their Associated Recommendations Transaction Report (unknown): “com exploiting the MetaMap tool (Aronson, 2001) to parse textual indications in the same manner described in Gottlieb et al (2011).”
Towards Internet-Age Pharmacovigilance: Extracting Adverse Drug Reactions from User Posts in Health-Related Social Networks (2010, methodology): “MetaMap is a primarily lexical system for mapping concepts in biomedical text to concepts in the UMLS Metathesaurus (Aronson, 2001).”
Mining electronic health records: towards better research applications and clinical care (2012, methodology): “In the entity recognition step, NPs and various lexical permutations are then mapped to controlled vocabularies using tools such as MetaMap 106 .”
Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features (2015, methodology): “We aimed to analyze the performance of ADRMine relative to four baseline techniques: two simple baselines based on MetaMap, 54 a lexicon-based approach for extraction of candidate ADR phrases based on our ADR lexicon, and an SVM classifier that identifies the semantic type of the extracted phrases.”
The Unified Medical Language System (UMLS): integrating biomedical terminology (2004, background): “MetaMap ( 10 ), accessible as a web service, extracts Metathesaurus concepts from text.”
DNorm: disease name normalization with pairwise learning to rank (2013, unknown): “For each phrase, MetaMap identifies possible mappings to UMLS based on lexical lookup and on variants by associating a score with each one of them.”
BMC Medical Informatics and Decision Making (2006, unknown): “The MEDLEE system developed by Friedman et al [1], the Symtxt system developed by Haug [2], the statistical NLP tool by Taira et al [3], and the widely used MetaMap/ MMtx [4] by National Library of Medicine researchers are examples of medical NLP systems.”
ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing (2019, methodology): “MetaMap and MetaMapLite (Aronson, 2001; Demner-Fushman et al., 2017), the two most widely used and supported tools for biomedical text processing, support entity linking with negation detection and acronym resolution.”
Enhancing clinical concept extraction with distributional semantics (2011, methodology): “As is the case with MedLEE, MetaMap [11], an initiative of NLM to map text to the UMLS Metathesarus using a large lexicon, has been widely adopted for research purposes.”
Medical Question Answering for Clinical Decision Support (2016, methodology): “The resources used for feature extraction are The Unified Medical Language System (UMLS) [6], MetaMap [2], the GENIA project [13], WordNet [10], PropBank [14], the SwiRL semantic role labeler [28], and Wikipedia.”