Automated Extraction of BI-RADS Final Assessment Categories from Radiology Reports with Natural Language Processing.
The objective of this study is to evaluate a natural language processing (NLP) algorithm that determines American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) final assessment categories from radiology reports. This HIPAA-compliant study was granted institutional review bo...
| Publicado en: | Journal of Digital Imaging Vol. 26; no. 5; pp. 989 - 995 |
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| Autores principales: | , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2013
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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=104229504&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104229504 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2013 vid: 26 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104229504 90397222 10.1007/s10278-013-9616-5 NLM23868515 PMC3782591 104229504 ppf: 989 ppct: 6 formats: fmt: @attributes: type: P tig: atl: Automated Extraction of BI-RADS Final Assessment Categories from Radiology Reports with Natural Language Processing. aug: au: Sippo, Dorothy Warden, Graham Andriole, Katherine Lacson, Ronilda Ikuta, Ichiro Birdwell, Robyn Khorasani, Ramin affil: Medical Corp, United States Air Force, CMR 402 BOX 142, APO AE 09180-0002 Washington USA sug: subj: Algorithms Mammography Natural Language Processing Reports Human Confidence Intervals Cross Sectional Studies Precision Breast Neoplasms Diagnosis Patient Record Systems Breast Radiography Software Data Analysis Software Data Collection ab: The objective of this study is to evaluate a natural language processing (NLP) algorithm that determines American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) final assessment categories from radiology reports. This HIPAA-compliant study was granted institutional review board approval with waiver of informed consent. This cross-sectional study involved 1,165 breast imaging reports in the electronic medical record (EMR) from a tertiary care academic breast imaging center from 2009. Reports included screening mammography, diagnostic mammography, breast ultrasound, combined diagnostic mammography and breast ultrasound, and breast magnetic resonance imaging studies. Over 220 reports were included from each study type. The recall (sensitivity) and precision (positive predictive value) of a NLP algorithm to collect BI-RADS final assessment categories stated in the report final text was evaluated against a manual human review standard reference. For all breast imaging reports, the NLP algorithm demonstrated a recall of 100.0 % (95 % confidence interval (CI), 99.7, 100.0 %) and a precision of 96.6 % (95 % CI, 95.4, 97.5 %) for correct identification of BI-RADS final assessment categories. The NLP algorithm demonstrated high recall and precision for extraction of BI-RADS final assessment categories from the free text of breast imaging reports. NLP may provide an accurate, scalable data extraction mechanism from reports within EMRs to create databases to track breast imaging performance measures and facilitate optimal breast cancer population management strategies. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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