Assessing Inaccuracies in Automated Information Extraction of Breast Imaging Findings.
We previously identified breast imaging findings from radiology reports using an expert-based information extraction algorithm as part of the National Cancer Institute's Population-based Research Optimizing Screening through Personalized Regimens (PROSPR) initiative. We validate this algorithm and a...
| Publicado en: | Journal of Digital Imaging Vol. 30; no. 2; pp. 228 - 234 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2017
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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=121962653&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 121962653 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2017 vid: 30 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 121962653 121962653 144057711 121962653 10.1007/s10278-016-9927-4 121962653 ppf: 228 ppct: 6 formats: fmt: @attributes: type: P tig: atl: Assessing Inaccuracies in Automated Information Extraction of Breast Imaging Findings. aug: au: Lacson, Ronilda Goodrich, Martha Harris, Kimberly Brawarsky, Phyllis Haas, Jennifer affil: Department of Biomedical Data Science , Geisel School of Medicine at Dartmouth , Lebanon USA sug: subj: Mammography Image Interpretation, Computer Assisted Breast Breast Neoplasms Female Human Radiology Information Systems Magnetic Resonance Imaging Algorithms Female ab: We previously identified breast imaging findings from radiology reports using an expert-based information extraction algorithm as part of the National Cancer Institute's Population-based Research Optimizing Screening through Personalized Regimens (PROSPR) initiative. We validate this algorithm and assess inaccuracies in a different institutional setting. Mammography, ultrasound (US), and breast magnetic resonance imaging (MRI) reports of patients at an academic health system between 4/2013 and 6/2013 were included for analysis. Accuracy of automatically extracting imaging findings using an algorithm developed at a different institution compared to manual gold standard review is reported. Extraction errors are further categorized based on manual review. Precision and recall for extracting BI-RADS categories remain between 0.9 and 1.0, except for MRI (0.7). F measures for extracting other findings are 0.9 for non-mass enhancement (in MRI) and 0.8-0.9 for cysts (in MRI and US). Extracting breast imaging findings resulted in lowest accuracy for findings of calcification (range 0.4-0.6 in mammography) and asymmetric density (0.5-0.7 in mammography). Majority of errors for extracting imaging findings were due to qualifier-based errors, descriptors which indicate absence of findings, missed by automated extraction (e.g., 'benign' calcifications). Our information extraction algorithm provides an effective approach to extracting some breast imaging findings for populating a breast screening registry. However, errors in information extraction when utilizing methods in new settings demonstrate that further work is necessary to extract information content from unstructured multi-institutional radiology reports. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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