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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
Affiliation:1. IBM T.J. Watson Research Center, 19 Skyline Drive, Hawthorne, NY 10532, USA;2. Division of Biomedical Informatics, Mayo Clinic College of Medicine, Rochester, MN, USA;3. Division of Gastroenterology and Hepatology, Mayo Clinic College of Medicine, Rochester, MN, USA;4. Georgia Institute of Technology, Atlanta, GA, USA;1. Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15260, USA;2. Center for Computational Pharmacology, Biomedical Text Mining Group, University of Colorado School of Medicine, Denver, CO 80202
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.
Keywords:
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