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Combining an Expert-Based Medical Entity Recognizer to a Machine-Learning System: Methods and a Case Study.

Medical entity recognition is currently generally performed by data-driven methods based on supervised machine learning. Expert-based systems, where linguistic and domain expertise are directly provided to the system are often combined with data-driven systems. We present here a case study where an...

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Published in:Biomedical Informatics Insights no. 6; pp. 51 - 63
Main Authors: Zweigenbaum, Pierre, Lavergne, Thomas, Grabar, Natalia, Hamon, Thierry, Rosset, Sophie, Grouin, Cyril
Format: research tables/charts Journal Article
Published: Sage Publications Inc. 2013 Supplement
Online Access:View this record in EBSCOhost
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      dt: 2013 Supplement
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        atl: Combining an Expert-Based Medical Entity Recognizer to a Machine-Learning System: Methods and a Case Study.
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        au:
          Zweigenbaum, Pierre
          Lavergne, Thomas
          Grabar, Natalia
          Hamon, Thierry
          Rosset, Sophie
          Grouin, Cyril
        affil: LIMSI-CNS, Orsay, France
      sug:
        subj:
          Natural Language Processing
          Medical Records
          Nomenclature
          Artificial Intelligence
          Human
          Data Mining
          Unified Medical Language System
          Funding Source
      ab: Medical entity recognition is currently generally performed by data-driven methods based on supervised machine learning. Expert-based systems, where linguistic and domain expertise are directly provided to the system are often combined with data-driven systems. We present here a case study where an existing expert-based medical entity recognition system, Ogmios, is combined with a data-driven system, Caramba, based on a linear-chain Conditional Random Field (CRF) classifier. Our case study specifically highlights the risk of overfitting incurred by an expert-based system. We observe that it prevents the combination of the 2 systems from obtaining improvements in precision, recall, or F-measure, and analyze the underlying mechanisms through a post-hoc feature-level analysis. Wrapping the expert-based system alone as attributes input to a CRF classifier does boost its F-measure from 0.603 to 0.710, bringing it on par with the data-driven system. The generalization of this method remains to be further investigated.
      pubtype: Academic Journal
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        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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