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14.08.2017 | Ausgabe 1/2018

Journal of Digital Imaging 1/2018

Using Natural Language Processing of Free-Text Radiology Reports to Identify Type 1 Modic Endplate Changes

Journal of Digital Imaging > Ausgabe 1/2018
Hannu T. Huhdanpaa, W. Katherine Tan, Sean D. Rundell, Pradeep Suri, Falgun H. Chokshi, Bryan A. Comstock, Patrick J. Heagerty, Kathryn T. James, Andrew L. Avins, Srdjan S. Nedeljkovic, David R. Nerenz, David F. Kallmes, Patrick H. Luetmer, Karen J. Sherman, Nancy L. Organ, Brent Griffith, Curtis P. Langlotz, David Carrell, Saeed Hassanpour, Jeffrey G. Jarvik


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 N = 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.

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