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Pipeline publication

[…] tructured texts in EMRs: (i) named entity recognition (NER) (particularly, named entities of drug and event) and normalization, (ii) relation generation (drug-event candidates), and (iii) relation classification (ADR identification)., As the first subprocess, the medical NER aims to recognize a clinical term mentioned in EMRs. Another extended task, the normalization intends to unify a discovered clinical term into a conventional lexicon based on an identical semantic meaning or a concept, which can be referred through UMLS concept unique identifier (CUI) (https://www.nlm.nih.gov). Many researchers endeavor to deal with medical NER and normalization by developing computational tools such as cTAKES (http://ctakes.apache.org), FreeLing-Med, MetaMap (https://metamap.nlm.nih.gov), MedLEE (http://www.medlingmap.org/taxonomy/term/80), tmChem (https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/tmChem.html), DNorm (https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/DNorm.html), GATE (https://gate.ac.uk), or Stanford CoreNLP tool (http://stanfordnlp.github.io/CoreNLP). From , by employing medical NER and normalization, we can identify two drugs (i.e., ramipril and bacterium) and five events (i.e., allergy, facial swelling, HTN (hypertension), respiratory infection, and viral infection) from the given clinical texts. Then, the normalization task replaces a drug term or an event term with CUI. For example, a drug term ramipril is replaced with C0072973, or an event term HTN is replaced with C0020538, which refers t […]

Pipeline specifications

Software tools cTakes, MetaMap, tmChem