Ontology-guided feature engineering for clinical text classification

Volume: 45, Issue: 5, Pages: 992 - 998
Published: Oct 1, 2012
Abstract
In this study we present novel feature engineering techniques that leverage the biomedical domain knowledge encoded in the Unified Medical Language System (UMLS) to improve machine-learning based clinical text classification. Critical steps in clinical text classification include identification of features and passages relevant to the classification task, and representation of clinical text to enable discrimination between documents of different...
Paper Details
Title
Ontology-guided feature engineering for clinical text classification
Published Date
Oct 1, 2012
Volume
45
Issue
5
Pages
992 - 998
Citation AnalysisPro
  • Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
  • Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.