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

Authors:
Anni Coden;Guergana Savova;Igor Sominsky;Michael Tanenblatt;James Masanz;Karin Schuler;James Cooper;Wei Guan;Piet C. de Groen
Affiliations:
IBM T.J. Watson Research Center, 19 Skyline Drive, Hawthorne, NY 10532, USA;Division of Biomedical Informatics, Mayo Clinic College of Medicine, Rochester, MN, USA;IBM T.J. Watson Research Center, 19 Skyline Drive, Hawthorne, NY 10532, USA;IBM T.J. Watson Research Center, 19 Skyline Drive, Hawthorne, NY 10532, USA;Division of Biomedical Informatics, Mayo Clinic College of Medicine, Rochester, MN, USA;Division of Biomedical Informatics, Mayo Clinic College of Medicine, Rochester, MN, USA;IBM T.J. Watson Research Center, 19 Skyline Drive, Hawthorne, NY 10532, USA;Georgia Institute of Technology, Atlanta, GA, USA;Division of Biomedical Informatics, Mayo Clinic College of Medicine, Rochester, MN, USA and Division of Gastroenterology and Hepatology, Mayo Clinic College of Medicine, Rochester, MN, USA
Venue:
Journal of Biomedical Informatics
Year:
2009

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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.