Assessing the Utility of Automatic Cancer Registry Notifications Data Extraction from Free-Text Pathology Reports

Anthony N Nguyen, Julie Moore, John O'Dwyer, Shoni Philpot
2015 AMIA Annual Symposium Proceedings  
Cancer Registries record cancer data by reading and interpreting pathology cancer specimen reports. For some Registries this can be a manual process, which is labour and time intensive and subject to errors. A system for automatic extraction of cancer data from HL7 electronic free-text pathology reports has been proposed to improve the workflow efficiency of the Cancer Registry. The system is currently processing an incoming trickle feed of HL7 electronic pathology reports from across the state
more » ... of Queensland in Australia to produce an electronic cancer notification. Natural language processing and symbolic reasoning using SNOMED CT were adopted in the system; Queensland Cancer Registry business rules were also incorporated. A set of 220 unseen pathology reports selected from patients with a range of cancers was used to evaluate the performance of the system. The system achieved overall recall of 0.78, precision of 0.83 and F-measure of 0.80 over seven categories, namely, basis of diagnosis (3 classes), primary site (66 classes), laterality (5 classes), histological type (94 classes), histological grade (7 classes), metastasis site (19 classes) and metastatic status (2 classes). These results are encouraging given the large cross-section of cancers. The system allows for the provision of clinical coding support as well as indicative statistics on the current state of cancer, which is not otherwise available.
pmid:26958232 pmcid:PMC4765645 fatcat:scprtgnukrhhtbob4jrc7zf7sa