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

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 i...

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Bibliographic Details
Published in:Journal of biomedical informatics 2009-10, Vol.42 (5), p.937-949
Main Authors: Coden, Anni, Savova, Guergana, Sominsky, Igor, Tanenblatt, Michael, Masanz, James, Schuler, Karin, Cooper, James, Guan, Wei, de Groen, Piet C.
Format: Article
Language:English
Subjects:
Analysis system
Cancer Disease Knowledge Representation Model
Concept formation
Databases, Factual
Humans
Information retrieval
Information Storage and Retrieval - methods
Medical Informatics - methods
Medical Records
Models, Theoretical
Natural Language Processing
Neoplasms - pathology
Pattern Recognition, Automated - methods
Terminology as Topic
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Summary: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.
ISSN:1532-0464
1532-0480
DOI:10.1016/j.jbi.2008.12.005