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Automated extraction of Biomarker information from pathology reports
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Jeongeun | - |
dc.contributor.author | Song, Hyun-Je | - |
dc.contributor.author | Yoon, Eunsil | - |
dc.contributor.author | Park, Seong-Bae | - |
dc.contributor.author | Park, Sung-Hye | - |
dc.contributor.author | Seo, Jeong-Wook | - |
dc.contributor.author | Park, Peom | - |
dc.contributor.author | Choi, Jinwook | - |
dc.date.accessioned | 2018-07-23T07:39:39Z | - |
dc.date.available | 2018-07-23T16:40:46Z | - |
dc.date.issued | 2018-05-21 | - |
dc.identifier.citation | BMC Medical Informatics and Decision Making, 18(1):29 | ko_KR |
dc.identifier.issn | 1472-6947 | - |
dc.identifier.uri | https://hdl.handle.net/10371/142747 | - |
dc.description.abstract | Background
Pathology reports are written in free-text form, which precludes efficient data gathering. We aimed to overcome this limitation and design an automated system for extracting biomarker profiles from accumulated pathology reports. Methods We designed a new data model for representing biomarker knowledge. The automated system parses immunohistochemistry reports based on a slide paragraph unit defined as a set of immunohistochemistry findings obtained for the same tissue slide. Pathology reports are parsed using context-free grammar for immunohistochemistry, and using a tree-like structure for surgical pathology. The performance of the approach was validated on manually annotated pathology reports of 100 randomly selected patients managed at Seoul National University Hospital. Results High F-scores were obtained for parsing biomarker name and corresponding test results (0.999 and 0.998, respectively) from the immunohistochemistry reports, compared to relatively poor performance for parsing surgical pathology findings. However, applying the proposed approach to our single-center dataset revealed information on 221 unique biomarkers, which represents a richer result than biomarker profiles obtained based on the published literature. Owing to the data representation model, the proposed approach can associate biomarker profiles extracted from an immunohistochemistry report with corresponding pathology findings listed in one or more surgical pathology reports. Term variations are resolved by normalization to corresponding preferred terms determined by expanded dictionary look-up and text similarity-based search. Conclusions Our proposed approach for biomarker data extraction addresses key limitations regarding data representation and can handle reports prepared in the clinical setting, which often contain incomplete sentences, typographical errors, and inconsistent formatting. | ko_KR |
dc.description.sponsorship | This study was financially supported by a grant of the Korean Health Technology R&D project, Ministry of Health & Welfare, Republic of Korea (A112005) and by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (2010–0028631) | ko_KR |
dc.language.iso | en | ko_KR |
dc.publisher | BioMed Central | ko_KR |
dc.subject | Biomarkers | ko_KR |
dc.subject | Cancer disease knowledge representation model | ko_KR |
dc.subject | Pathology reports | ko_KR |
dc.subject | Natural language processing | ko_KR |
dc.subject | Clinical decision-making | ko_KR |
dc.title | Automated extraction of Biomarker information from pathology reports | ko_KR |
dc.type | Article | ko_KR |
dc.contributor.AlternativeAuthor | 이정근 | - |
dc.contributor.AlternativeAuthor | 송현제 | - |
dc.contributor.AlternativeAuthor | 윤은실 | - |
dc.contributor.AlternativeAuthor | 박성배 | - |
dc.contributor.AlternativeAuthor | 박성혜 | - |
dc.contributor.AlternativeAuthor | 서정욱 | - |
dc.contributor.AlternativeAuthor | 박범 | - |
dc.contributor.AlternativeAuthor | 최진욱 | - |
dc.identifier.doi | 10.1186/s12911-018-0609-7 | - |
dc.language.rfc3066 | en | - |
dc.rights.holder | The Author(s). | - |
dc.date.updated | 2018-05-27T03:36:50Z | - |
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