Automated extraction and normalization of findings from cancer-related free-text radiology reports.
We describe the performance of a particular natural language processing system that uses knowledge vectors to extract findings from radiology reports. LifeCode (A-Life Medical, Inc.) has been successfully coding reports for billing purposes for several years. In this study, we describe the use of LifeCode to code all findings within a set of 500 cancer-related radiology reports against a test set in which all findings were manually tagged. The system was trained with 1400 reports prior to running the test set.


RESULTS
LifeCode had a recall of 84.5% and precision of 95.7% in the coding of cancer-related radiology report findings.


CONCLUSIONS
Despite the use of a modest sized training set and minimal training iterations, when applied to cancer-related reports the system achieved recall and precision measures comparable to other reputable natural language processors in this domain.
Mamlin
Burke W
BW
Regenstrief Institute for Health Care, Indianapolis, Indiana, USA.
Heinze
Daniel T
DT
McDonald
Clement J
CJ
D005103:Expert Systems; D005568:Forms and Records Control / Q000379:methods; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D038801:International Classification of Diseases; D009323:Natural Language Processing; D009369:Neoplasms / Q000000981:diagnostic imaging*; D013902:Radiography, Thoracic / Q000145:classification*; D011873:Radiology Information Systems; D012984:Software; D018875:Vocabulary, Controlled