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 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.
Coden
Anni
A
IBM T.J. Watson Research Center, 19 Skyline Drive, Hawthorne, NY 10532, USA. anni@us.ibm.com
Savova
Guergana
G
Sominsky
Igor
I
Tanenblatt
Michael
M
Masanz
James
J
Schuler
Karin
K
Cooper
James
J
Guan
Wei
W
de Groen
Piet C
PC
D016208:Databases, Factual; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008490:Medical Informatics / Q000379:methods; D008499:Medical Records; D008962:Models, Theoretical; D009323:Natural Language Processing; D009369:Neoplasms / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D009626:Terminology as Topic