Proposing New RadLex Terms by Analyzing Free-Text Mammography Reports.
After years of development, the RadLex terminology contains a large set of controlled terms for the radiology domain, but gaps still exist. We developed a data-driven approach to discover new terms for RadLex by mining a large corpus of radiology reports using natural language processing (NLP) metho...
| Publicado en: | Journal of Digital Imaging Vol. 31; no. 5; pp. 596 - 604 |
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| Autores principales: | , , , , |
| Formato: | algorithm forms research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=131880806&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 131880806 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2018 vid: 31 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 131880806 131880806 131880806 10.1007/s10278-018-0064-0 131880806 ppf: 596 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Proposing New RadLex Terms by Analyzing Free-Text Mammography Reports. aug: au: Bulu, Hakan Rubin, Daniel L. Sippo, Dorothy A. Lee, Janie M. Burnside, Elizabeth S. affil: Department of Radiology and Department of Biomedical Data Science, Medical School Office Building (MSOB), Stanford University, 1265 Welch Road, X383, 94305-5464, Stanford, CA, USA sug: subj: Mammography Software Data Mining Algorithms Human Reports Sensitivity and Specificity Radiologists Predictive Value of Tests Academic Medical Centers Precision ab: After years of development, the RadLex terminology contains a large set of controlled terms for the radiology domain, but gaps still exist. We developed a data-driven approach to discover new terms for RadLex by mining a large corpus of radiology reports using natural language processing (NLP) methods. Our system, developed for mammography, discovers new candidate terms by analyzing noun phrases in free-text reports to extend the mammography part of RadLex. Our NLP system extracts noun phrases from free-text mammography reports and classifies these noun phrases as “Has Candidate RadLex Term” or “Does Not Have Candidate RadLex Term.” We tested the performance of our algorithm using 100 free-text mammography reports. An expert radiologist determined the true positive and true negative RadLex candidate terms. We calculated precision/positive predictive value and recall/sensitivity metrics to judge the system’s performance. Finally, to identify new candidate terms for enhancing RadLex, we applied our NLP method to 270,540 free-text mammography reports obtained from three academic institutions. Our method demonstrated precision/positive predictive value of 0.77 (159/206 terms) and a recall/sensitivity of 0.94 (159/170 terms). The overall accuracy of the system is 0.80 (235/293 terms). When we ran our system on the set of 270,540 reports, it found 31,800 unique noun phrases that are potential candidates for RadLex. Our data-driven approach to mining radiology reports can identify new candidate terms for expanding the breast imaging lexicon portion of RadLex and may be a useful approach for discovering new candidate terms from other radiology domains. pubtype: Academic Journal doctype: algorithm forms research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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