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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We aim to build and evaluate an open-source natural language processing system for information extraction from electronic medical record clinical free-text. We describe and evaluate our system, the clinical Text Analysis and Knowledge Extraction System (cTAKES), released open-source at . The cTAKES builds on existing open-source technologies—the Unstructured Information Management Architecture framework and OpenNLP natural language processing toolkit. Its components, specifically trained for the clinical domain, create rich linguistic and semantic annotations. Performance of individual components: sentence boundary detector accuracy=0.949; tokenizer accuracy=0.949; part-of-speech tagger accuracy=0.936; shallow parser F-score=0.924; named entity recognizer and system-level evaluation F-score=0.715 for exact and 0.824 for overlapping spans, and accuracy for concept mapping, negation, and status attributes for exact and overlapping spans of 0.957, 0.943, 0.859, and 0.580, 0.939, and 0.839, respectively. Overall performance is discussed against five applications. The cTAKES annotations are the foundation for methods and modules for higher-level semantic processing of clinical free-text. The various ways in which one can refer to the same clinical concept needs to be accounted for in a semantic resource such as SNOMED CT. Developing terminological resources manually is, however, prohibitively expensive and likely to result in low coverage, especially given the high variability of language use in clinical text. To support this process, distributional methods can be employed in conjunction with a large corpus of electronic health records to extract synonym candidates for clinical terms. In this paper, we exemplify the potential of our proposed method using the Swedish version of SNOMED CT, which currently lacks synonyms. A medical expert inspects two thousand term pairs generated by two semantic spaces ‐ one of which models multiword terms in addition to single words ‐ for one hundred preferred terms of the semantic types disorder and finding. 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. SNOMED is a large description logic based terminology for recording in electronic health records. Often, neither the labels nor the description logic definitions are easy for users to understand. Furthermore, information is increasingly being recorded not just using individual SNOMED concepts but also using complex expressions in the description logic ("post-coordinated" concepts). Such post-coordinated expressions are likely to be even more complex than other definitions, and therefore can have no pre-assigned labels. Automatic verbalisation will be useful both for understanding and quality assurance of SNOMED definitions, and for helping users to understand post-coordinated expressions. OntoVerbal is a system that presents a compositional terminology expressed in OWL as natural language. We describe the application of OntoVerbal to SNOMED-CT, whereby SNOMED classes are presented as textual paragraphs through the use of natural language generation technology.