HTP-NLP: A New NLP System for High Throughput Phenotyping.

Journal: Studies in health technology and informatics
PMID: 28423797

Abstract

Secondary use of clinical data for research requires a method to quickly process the data so that researchers can quickly extract cohorts. We present two advances in the High Throughput Phenotyping NLP system which support the aim of truly high throughput processing of clinical data, inspired by a characterization of the linguistic properties of such data. Semantic indexing to store and generalize partially-processed results and the use of compositional expressions for ungrammatical text are discussed, along with a set of initial timing results for the system.

Authors

  • Daniel R Schlegel
    Department of Biomedical Informatics, University at Buffalo, SUNY, Buffalo, NY, USA.
  • Chris Crowner
    Department of Biomedical Informatics, University at Buffalo, SUNY, Buffalo, NY, USA.
  • Frank Lehoullier
    Department of Biomedical Informatics, University at Buffalo, Buffalo, NY, USA.
  • Peter L Elkin
    Department of Biomedical Informatics, University at Buffalo, Buffalo, NY.

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