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 http://www.ohnlp.org. 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.
We present a comparative study between two machine learning methods, Conditional Random Fields and Support Vector Machines for clinical named entity recognition. We explore their applicability to clinical domain. Evaluation against a set of gold standard named entities shows that CRFs outperform SVMs. The best F-score with CRFs is 0.86 and for the SVMs is 0.64 as compared to a baseline of 0.60.
SummaryObjectives: We examine recent published research on the extraction of information from textual documents in the Electronic Health Record (EHR). Methods: Literature review of the research published after 1995, based on PubMed, conference proceedings, and the ACM Digital Library, as well as on relevant publications referenced in papers already included. Results: 174 publications were selected and are discussed in this review in terms of methods used, pre-processing of textual documents, contextual features detection and analysis, extraction of information in general, extraction of codes and of information for decision-support and enrichment of the EHR, information extraction for surveillance, research, automated terminology management, and data mining, and de-identification of clinical text. Conclusions: Performance of information extraction systems with clinical text has improved since the last systematic review in 1995, but they are still rarely applied outside of the laboratory they have been developed in. Competitive challenges for information extraction from clinical text, along with the availability of annotated clinical text corpora, and further improvements in system performance are important factors to stimulate advances in this field and to increase the acceptance and usage of these systems in concrete clinical and biomedical research contexts. KeywordsElectronic health record, natural language processing, information extraction, text mining, state-of-the-art review
Abstract. Levin-style classes which capture the shared syntax and semantics of verbs have proven useful for many Natural Language Processing (NLP) tasks and applications. However, lexical resources which provide information about such classes are only available for a handful of worlds languages. Because manual development of such resources is extremely time consuming and cannot reliably capture domain variation in classification, methods for automatic induction of verb classes from texts have gained popularity. However, to date such methods have been applied to English and a handful of other, mainly resource-rich languages. In this paper, we apply the methods to Brazilian Portuguese -a language for which no VerbNet or automatic class induction work exists yet. Since Levinstyle classification is said to have a strong cross-linguistic component, we use unsupervised clustering techniques similar to those developed for English without language-specific feature engineering. This yields interesting results which line up well with those obtained for other languages, demonstrating the crosslinguistic nature of this type of classification. However, we also discover and discuss issues which require specific consideration when aiming to optimise the performance of verb clustering for Brazilian Portuguese and other less-resourced languages.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.