Automatically extracting cancer disease characteristics from pathology reports into a Disease Knowledge Representation Model

Anni Coden, Guergana Savova, Igor Sominsky, Michael Tanenblatt, James Masanz, Karin Schuler, James Cooper, Wei Guan, Piet C. de Groen

Research output: Contribution to journalArticlepeer-review

108 Scopus citations

Abstract

We introduce an extensible and modifiable knowledge representation model to represent cancer disease characteristics in a comparable and consistent fashion. We describe a system, MedTAS/P which automatically instantiates the knowledge representation model from free-text pathology reports. MedTAS/P is based on an open-source framework and its components use natural language processing principles, machine learning and rules to discover and populate elements of the model. To validate the model and measure the accuracy of MedTAS/P, we developed a gold-standard corpus of manually annotated colon cancer pathology reports. MedTAS/P achieves F1-scores of 0.97-1.0 for instantiating classes in the knowledge representation model such as histologies or anatomical sites, and F1-scores of 0.82-0.93 for primary tumors or lymph nodes, which require the extractions of relations. An F1-score of 0.65 is reported for metastatic tumors, a lower score predominantly due to a very small number of instances in the training and test sets.

Original languageEnglish (US)
Pages (from-to)937-949
Number of pages13
JournalJournal of Biomedical Informatics
Volume42
Issue number5
DOIs
StatePublished - Oct 1 2009

Keywords

  • Analysis system
  • Cancer Disease Knowledge Representation Model
  • Concept formation
  • Information retrieval
  • Medical records
  • Natural language processing

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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