Normalization and standardization of electronic health records for high-throughput phenotyping: The sharpn consortium Academic Article uri icon

Overview

MeSH Major

  • Data Mining
  • Electronic Health Records
  • Medical Informatics Applications
  • Natural Language Processing
  • Phenotype

abstract

  • End-to-end automated systems for extracting clinical information from diverse EHR systems require extensive use of standardized vocabularies and terminologies, as well as robust information models for storing, discovering, and processing that information. This study demonstrates the application of modular and open-source resources for enabling secondary use of EHR data through normalization into standards-based, comparable, and consistent format for high-throughput phenotyping to identify patient cohorts.

authors

publication date

  • December 23, 2013

Research

keywords

  • Academic Article

Identity

Language

  • eng

PubMed Central ID

  • PMC3861933

Digital Object Identifier (DOI)

  • 10.1136/amiajnl-2013-001939

PubMed ID

  • 24190931

Additional Document Info

start page

  • e341

end page

  • 8

volume

  • 20

number

  • E2