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.
@cite_1 is a comprehensive platform for performing many clinical information extraction tasks, including enriching text with terms from the UMLS Metathesaurus. cTakes does dictionary lookup to recognize and identify clinical entities. They report that mapping to the UMLS accuracy is high for exact span matches.
@cite_1
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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. Objective : Development of a general natural-language processor that identifies clinical information in narrative reports and maps that information into a structured representation containing clinical terms. @PARASPLIT Design : The natural-language processor provides three phases of processing, all of which are driven by different knowledge sources. The first phase performs the parsing. It identifies the structure of the text through use of a grammar that defines semantic patterns and a target form. The second phase, regularization, standardizes the terms in the initial target structure via a compositional mapping of multi-word phrases. The third phase, encoding, maps the terms to a controlled vocabulary. Radiology is the test domain for the processor and the target structure is a formal model for representing clinical information in that domain. @PARASPLIT Measurements : The impression sections of 230 radiology reports were encoded by the processor. Results of an automated query of the resultant database for the occurrences of four diseases were compared with the analysis of a panel of three physicians to determine recall and precision. @PARASPLIT Results : Without training specific to the four diseases, recall and precision of the system(combined effect of the processor and query generator) were 70 and 87 . Training of the query component increased recall to 85 without changing precision. MetaMap is an online application that allows mapping text to UMLS Metathesaurus concepts, which is very useful interoperability among different languages and systems within the biomedical domain. MetaMap Transfer (MMTx) is a Java program that makes MetaMap available to biomedical researchers. Currently there is no Spanish version of MetaMap, which difficults the use of UMLS Metathesaurus to extract concepts from Spanish biomedical texts. Our ongoing research is mainly focused on using biomedical concepts for cross-lingual text classification and retrieval [3]. In this context the use of concepts instead of bag of words representation allows us to face text classification tasks abstracting from the language [4]. In this paper we evaluate the possibility of combining automatic translation techniques with the use of biomedical ontologies to produce an English text that can be processed by MMTx. Citations play an essential role in navigating academic literature and following chains of evidence in research. With the growing availability of large digital archives of scientific papers, the automated extraction and analysis of citations is becoming increasingly relevant. However, existing approaches to citation extraction still fall short of the high accuracy required to build more sophisticated and reliable tools for citation analysis and corpus navigation. In this paper, we present techniques for high accuracy extraction of citations and references from academic papers. By collecting multiple sources of evidence about entities from documents, and integrating citation extraction, reference segmentation, and citation-reference matching, we are able to significantly improve performance in subtasks including citation identification, author named entity recognition, and citation-reference matching. Applying our algorithm to previously-unseen documents, we demonstrate high F-measure performance of 0.980 for citation extraction, 0.983 for author named entity recognition, and 0.948 for citation-reference matching.