Linked Life Data

19135551

Source:http://linkedlifedata.com/resource/pubmed/id/19135551

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5
pubmed:dateCreated
2009-10-5
pubmed:abstractText
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.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
1532-0480
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
42
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
937-49
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Automatically extracting cancer disease characteristics from pathology reports into a Disease Knowledge Representation Model.
pubmed:affiliation
IBM T.J. Watson Research Center, 19 Skyline Drive, Hawthorne, NY 10532, USA. anni@us.ibm.com
pubmed:publicationType
Journal Article