High-performance tagging on medical texts

Hahn, Udo; Wermter, Joachim

doi:10.3115/1220355.1220495

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…Coden et al (2005) achieved a POS tagging accuracy of 87% when applying a general English language tagger to clinical data, and a more recent study reported a highest accuracy of 88.6% when applied to clinical data (Ferraro et al 2013). On the other hand, Hahn & Wermter (2004) achieved surprisingly high accuracy rates thereby refuting previous claims by Campbell & Johnson (2001) that general language off-the-shelf taggers cannot be used on medical text without adaptation. They found that the statistical tagger TnT, when only trained on a German language newspaper corpus (NEGRA) and subsequently applied to a clinical corpus achieved an accuracy of 95.2%.…”

Section: Automatic Analysis Of Clinical Textcontrasting

confidence: 66%

Professional language in Swedish clinical text: Linguistic characterization and comparative studies

et al. 2014

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This study investigates the linguistic characteristics of Swedish clinical text in radiology reports and doctor's daily notes from electronic health records (EHRs) in comparison to general Swedish and biomedical journal text. We quantify linguistic features through a comparative register analysis to determine how the free text of EHRs differ from general and biomedical Swedish text in terms of lexical complexity, word and sentence composition, and common sentence structures. The linguistic features are extracted using state-of-the-art computational tools: a tokenizer, a part-of-speech tagger, and scripts for statistical analysis. Results show that technical terms and abbreviations are more frequent in clinical text, and lexical variance is low. Moreover, clinical text frequently omit subjects, verbs, and function words resulting in shorter sentences. Clinical text not only differs from general Swedish, but also internally, across its sub-domains, e.g. sentences lacking verbs are significantly more frequent in radiology reports. These results provide a foundation for future development of automatic methods for EHR simplification or clarification.

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Section: Automatic Analysis Of Clinical Textcontrasting

confidence: 66%

Professional language in Swedish clinical text: Linguistic characterization and comparative studies

et al. 2014

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“…There has also been research considering clinical natural language processing tasks in German, for instance, sentence boundary and abbreviation detection [ 30 ] or part-of-speech tagging [ 31 ]. In 2002, Hahn et al [ 4 ] described a system for the extraction of information from findings reports, called MEDSYNDIKATE, which heavily builds upon syntactic parsing and handcrafted or automatically assembled domain knowledge.…”

Section: Introductionmentioning

confidence: 99%

Fine-grained information extraction from German transthoracic echocardiography reports

Toepfer

Corovic

Fette

et al. 2015

BMC Med Inform Decis Mak

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BackgroundInformation extraction techniques that get structured representations out of unstructured data make a large amount of clinically relevant information about patients accessible for semantic applications. These methods typically rely on standardized terminologies that guide this process. Many languages and clinical domains, however, lack appropriate resources and tools, as well as evaluations of their applications, especially if detailed conceptualizations of the domain are required. For instance, German transthoracic echocardiography reports have not been targeted sufficiently before, despite of their importance for clinical trials. This work therefore aimed at development and evaluation of an information extraction component with a fine-grained terminology that enables to recognize almost all relevant information stated in German transthoracic echocardiography reports at the University Hospital of Würzburg.MethodsA domain expert validated and iteratively refined an automatically inferred base terminology. The terminology was used by an ontology-driven information extraction system that outputs attribute value pairs. The final component has been mapped to the central elements of a standardized terminology, and it has been evaluated according to documents with different layouts.ResultsThe final system achieved state-of-the-art precision (micro average.996) and recall (micro average.961) on 100 test documents that represent more than 90 % of all reports. In particular, principal aspects as defined in a standardized external terminology were recognized with f1=.989 (micro average) and f1=.963 (macro average). As a result of keyword matching and restraint concept extraction, the system obtained high precision also on unstructured or exceptionally short documents, and documents with uncommon layout.ConclusionsThe developed terminology and the proposed information extraction system allow to extract fine-grained information from German semi-structured transthoracic echocardiography reports with very high precision and high recall on the majority of documents at the University Hospital of Würzburg. Extracted results populate a clinical data warehouse which supports clinical research.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-015-0215-x) contains supplementary material, which is available to authorized users.

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“…The addition of an 800-word domain-specific lexicon revealed a performance increase of 5% and selecting sentences that contained the most frequent unknown words proved to be most helpful. Hahn et al [47] investigated the use of a rule-based POS-tagger (Brill tagger) and a statistical tagger (TnT) on clinical data. The statistical tagger TnT trained on general texts performed close to the state of the art in the medical domain.…”

Section: State Of the Art In Information Extraction From The Ehr Methmentioning

confidence: 99%

Extracting Information from Textual Documents in the Electronic Health Record: A Review of Recent Research

Meystre¹,

Savova²,

Kipper-Schuler³

et al. 2008

Yearb Med Inform

373

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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

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High-performance tagging on medical texts

Cited by 6 publications

References 11 publications

Professional language in Swedish clinical text: Linguistic characterization and comparative studies

Professional language in Swedish clinical text: Linguistic characterization and comparative studies

Fine-grained information extraction from German transthoracic echocardiography reports

Extracting Information from Textual Documents in the Electronic Health Record: A Review of Recent Research

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