Journal of Pathology Informatics Journal of Pathology Informatics
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ORIGINAL ARTICLE
Year : 2017  |  Volume : 8  |  Issue : 1  |  Page : 48

Application of text information extraction system for real-time cancer case identification in an integrated healthcare organization


Department of Research and Evaluation, Kaiser Permanente Southern California Medical Group, Pasadena, CA, USA

Correspondence Address:
Dr. Fagen Xie
Department of Research and Evaluation, Kaiser Permanente Southern California Medical Group, 100 S Los Robles Ave, 2nd Floor, Pasadena CA 91101
USA
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jpi.jpi_55_17

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Background: Surgical pathology reports (SPR) contain rich clinical diagnosis information. The text information extraction system (TIES) is an end-to-end application leveraging natural language processing technologies and focused on the processing of pathology and/or radiology reports. Methods: We deployed the TIES system and integrated SPRs into the TIES system on a daily basis at Kaiser Permanente Southern California. The breast cancer cases diagnosed in December 2013 from the Cancer Registry (CANREG) were used to validate the performance of the TIES system. The National Cancer Institute Metathesaurus (NCIM) concept terms and codes to describe breast cancer were identified through the Unified Medical Language System Terminology Service (UTS) application. The identified NCIM codes were used to search for the coded SPRs in the back-end datastore directly. The identified cases were then compared with the breast cancer patients pulled from CANREG. Results: A total of 437 breast cancer concept terms and 14 combinations of “breast” and “cancer” terms were identified from the UTS application. A total of 249 breast cancer cases diagnosed in December 2013 was pulled from CANREG. Out of these 249 cases, 241 were successfully identified by the TIES system from a total of 457 reports. The TIES system also identified an additional 277 cases that were not part of the validation sample. Out of the 277 cases, 11% were determined as highly likely to be cases after manual examinations, and 86% were in CANREG but were diagnosed in months other than December of 2013. Conclusions: The study demonstrated that the TIES system can effectively identify potential breast cancer cases in our care setting. Identified potential cases can be easily confirmed by reviewing the corresponding annotated reports through the front-end visualization interface. The TIES system is a great tool for identifying potential various cancer cases in a timely manner and on a regular basis in support of clinical research studies.


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