Multi-task Deep Neural Networks for Automated Extraction of Primary Site and Laterality Information from Cancer Pathology Reports
Automated annotation of free-text cancer pathology reports is a critical challenge for cancer registries and the national cancer surveillance program. In this paper, we investigated deep neural networks (DNNs) for automated extraction of the primary cancer site and its laterality, two fundamental targets of cancer reporting. Our experiments showed that single-task DNNs are capable of extracting information with higher precision and recall than traditional classification methods for the more challenging target. Furthermore, a multi-task learning DNN resulted in further performance improvement. This preliminary study, indicate the strong potential for multi-task deep neural networks to extract cancer-relevant information from free-text pathology reports.
KeywordsMulti-task learning Deep neural network Cancer pathology report Natural language processing
This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).
The study was supported by the Laboratory Directed Research and Development (LDRD) program of Oak Ridge National Laboratory, under LDRD projects No. 7417 and No. 8231.
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