This paper presents a novel prototype for biomedical term normalization of electronic health record excerpts with the Unified Medical Language System (UMLS) Metathesaurus. Despite being multilingual and cross-lingual by design, we first focus on processing clinical text in Spanish because there is no existing tool for this language and for this specific purpose. The tool is based on Apache Lucene to index the Metathesaurus and generate mapping candidates from input text. It uses the IXA pipeline for basic language processing and resolves ambiguities with the UKB toolkit. It has been evaluated by measuring its agreement with MetaMap in two English-Spanish parallel corpora. In addition, we present a web-based interface for the tool.
The system by aims at retrieving SNOMED CT concepts based on an input phrase (SNOMED CT is the most complete biomedical terminology, and it is included in the UMLS). Term normalization is done by querying an Apache Lucene index of SNOMED CT and re-ranking the candidates with a function of their own. In order to evaluate the performance of this system, they obtained a set of 100 health records manually tagged by two specialists with disruptions'' or procedures'' concepts in SNOMED CT . For the exact-matching assessment, they report an average precision of 39 @cite_9 uses the Freeling analyzer @cite_11 and extend its linguistic data with various knowledge sources including SNOMED CT , a list of medical abbreviations @cite_6 , Bot PLUS, and ICD-9. The actual task that the tool is meant to perform is term recognition, not term normalization. The system was assessed against a Gold Standard of 100 health records annotated with drug names, diseases and substances, counting as true positives approximate matches. The final result was 0.90 per the F-measure.
@cite_9 @cite_6 @cite_11
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This paper presents an annotation tool that detects entities in the biomedical domain. By enriching the lexica of the Freeling analyzer with bio-medical terms extracted from dictionaries and ontologies as SNOMED CT, the system is able to automatically detect medical terms in texts. An evaluation has been performed against a manually tagged corpus focusing on entities referring to pharmaceutical drug-names, substances and diseases. The obtained results show that a good annotation tool would help to leverage subsequent processes as data mining or pattern recognition tasks in the biomedical domain. Basic language processing such as tokenizing, morphological analyzers, lemmatizing, PoS tagging, chunking, etc. is a need for most NL applications such as Machine Translation, Summarization, Dialogue systems, etc. A large part of the effort required to develop such applications is devoted to the adaptation of existing software resources to the platform, programming language, format or API of the nal system. In LREC’02, we presented the object architecture that we are currently using (Carreras and Padr· o, 2002), which enables the quick and easy integration of basic language analyzers in any NLP application. Now we present a suite of analysis tools based on that architecture, which is distributed under Lesser General Public License (LGPL) (Free Software Foundation, 1999). The rst release of the suite will include morphological analyzer and Part-of-Speech tagger for English, Spanish, and Catalan.