Challenges in clinical natural language processing for automated disorder normalization.
Background: Identifying key variables such as disorders within the clinical narratives in electronic health records has wide-ranging applications within clinical practice and biomedical research. Previous research has demonstrated reduced performance of disorder named entity recognition (NER) and no...
| Publicado en: | Journal of Biomedical Informatics Vol. 57; pp. 28 - 38 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Academic Press Inc.
Oct2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=110866349&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 110866349 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Oct2015 vid: 57 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 110866349 110866349 NLM26187250 110866349 10.1016/j.jbi.2015.07.010 NLM26187250 PMC4713367 [Available on 10/01/16] 110866349 ppf: 28 ppct: 10 formats: tig: atl: Challenges in clinical natural language processing for automated disorder normalization. aug: au: Leaman, Robert Khare, Ritu Lu, Zhiyong affil: National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, United States sug: subj: Natural Language Processing Vocabulary, Controlled Software Narratives Funding Source Human ab: Background: Identifying key variables such as disorders within the clinical narratives in electronic health records has wide-ranging applications within clinical practice and biomedical research. Previous research has demonstrated reduced performance of disorder named entity recognition (NER) and normalization (or grounding) in clinical narratives than in biomedical publications. In this work, we aim to identify the cause for this performance difference and introduce general solutions.Methods: We use closure properties to compare the richness of the vocabulary in clinical narrative text to biomedical publications. We approach both disorder NER and normalization using machine learning methodologies. Our NER methodology is based on linear-chain conditional random fields with a rich feature approach, and we introduce several improvements to enhance the lexical knowledge of the NER system. Our normalization method - never previously applied to clinical data - uses pairwise learning to rank to automatically learn term variation directly from the training data.Results: We find that while the size of the overall vocabulary is similar between clinical narrative and biomedical publications, clinical narrative uses a richer terminology to describe disorders than publications. We apply our system, DNorm-C, to locate disorder mentions and in the clinical narratives from the recent ShARe/CLEF eHealth Task. For NER (strict span-only), our system achieves precision=0.797, recall=0.713, f-score=0.753. For the normalization task (strict span+concept) it achieves precision=0.712, recall=0.637, f-score=0.672. The improvements described in this article increase the NER f-score by 0.039 and the normalization f-score by 0.036. We also describe a high recall version of the NER, which increases the normalization recall to as high as 0.744, albeit with reduced precision.Discussion: We perform an error analysis, demonstrating that NER errors outnumber normalization errors by more than 4-to-1. Abbreviations and acronyms are found to be frequent causes of error, in addition to the mentions the annotators were not able to identify within the scope of the controlled vocabulary.Conclusion: Disorder mentions in text from clinical narratives use a rich vocabulary that results in high term variation, which we believe to be one of the primary causes of reduced performance in clinical narrative. We show that pairwise learning to rank offers high performance in this context, and introduce several lexical enhancements - generalizable to other clinical NER tasks - that improve the ability of the NER system to handle this variation. DNorm-C is a high performing, open source system for disorders in clinical text, and a promising step toward NER and normalization methods that are trainable to a wide variety of domains and entities. (DNorm-C is open source software, and is available with a trained model at the DNorm demonstration website: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/#DNorm.). pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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