Using Natural Language Processing of Free-Text Radiology Reports to Identify Type 1 Modic Endplate Changes.
Electronic medical record (EMR) systems provide easy access to radiology reports and offer great potential to support quality improvement efforts and clinical research. Harnessing the full potential of the EMR requires scalable approaches such as natural language processing (NLP) to convert text int...
| Publicado en: | Journal of Digital Imaging Vol. 31; no. 1; pp. 84 - 91 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2018
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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=127707185&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 127707185 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2018 vid: 31 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 127707185 127707185 144038572 127707185 10.1007/s10278-017-0013-3 127707185 ppf: 84 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Using Natural Language Processing of Free-Text Radiology Reports to Identify Type 1 Modic Endplate Changes. aug: au: Huhdanpaa, Hannu T. Tan, W. Katherine Rundell, Sean D. Suri, Pradeep Chokshi, Falgun H. Comstock, Bryan A. Heagerty, Patrick J. James, Kathryn T. Avins, Andrew L. Nedeljkovic, Srdjan S. Nerenz, David R. Kallmes, David F. Luetmer, Patrick H. Sherman, Karen J. Organ, Nancy L. Griffith, Brent Langlotz, Curtis P. Carrell, David Hassanpour, Saeed Jarvik, Jeffrey G. affil: Radia, Inc., Lynwood, WA, USA sug: subj: Patient Record Systems Natural Language Processing Lumbar Vertebrae Radiography Human Spinal Diseases Radiography kappa Statistic Confidence Intervals ab: Electronic medical record (EMR) systems provide easy access to radiology reports and offer great potential to support quality improvement efforts and clinical research. Harnessing the full potential of the EMR requires scalable approaches such as natural language processing (NLP) to convert text into variables used for evaluation or analysis. Our goal was to determine the feasibility of using NLP to identify patients with Type 1 Modic endplate changes using clinical reports of magnetic resonance (MR) imaging examinations of the spine. Identifying patients with Type 1 Modic change who may be eligible for clinical trials is important as these findings may be important targets for intervention. Four annotators identified all reports that contained Type 1 Modic change, using <italic>N</italic> = 458 randomly selected lumbar spine MR reports. We then implemented a rule-based NLP algorithm in Java using regular expressions. The prevalence of Type 1 Modic change in the annotated dataset was 10%. Results were recall (sensitivity) 35/50 = 0.70 (95% confidence interval (C.I.) 0.52–0.82), specificity 404/408 = 0.99 (0.97–1.0), precision (positive predictive value) 35/39 = 0.90 (0.75–0.97), negative predictive value 404/419 = 0.96 (0.94–0.98), and F1-score 0.79 (0.43–1.0). Our evaluation shows the efficacy of rule-based NLP approach for identifying patients with Type 1 Modic change if the emphasis is on identifying only relevant cases with low concern regarding false negatives. As expected, our results show that specificity is higher than recall. This is due to the inherent difficulty of eliciting all possible keywords given the enormous variability of lumbar spine reporting, which decreases recall, while availability of good negation algorithms improves specificity. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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