# Using Natural Language Processing to Identify Low Back Pain in Imaging Reports
## 4 Discussion  
The purpose of study was the development of the NLP system for extracting the clinical findings, which are associated with LPB on X-ray, CT and MRI radiologic reports. For NLP system modeling, the training datasets (\(n\) = 720; 240 X-ray, 240 CT. and 240 MRI) and the test datasets (\(n\) = 120; 40 X-ray, 40 CT, and 40 MRI) were extracted from 18,640 radiologic reports. Between the training and test datasets, 19/23 radiologic findings show no statistically significant differences. In this study, we developed a rule-based NLP pipeline for identifying 23 radiologic findings in X-ray, CT, and MRI reports. In detail, regular expression and negation detection were used for modeling. In assessing the performance of this NLP system, there are four parameters for validation--recall, precision, accuracy, and F1 score. This NLP system presented accuracy more than 0.9611. To correct the effects of dataset bias, the NLP system was evaluated using the F1 score. The F1 score is the most important thing among the parameters for NLP system evaluation. The F1 score is an indicator that reflects recall and precision. In this NLP system, all radiologic findings had an F1 score of 0.9802 or higher. Recall means the sensitivity of the statistics. The developed NLP system had a sensitivity of at least 0.984. Compared to the previous rule-based NLP system [16], the sensitivity of the rule-based NLP pipeline was improved. From the previous study, the potentially clinically important radiologic findings were defined--Disc extrusion, endplate edema or Type 1 Modic, any stenosis, central stenosis, foraminal stenosis, nerve root displaced/compressed, and lateral recess stenosis [16]. In this study, the 7 potentially clinically important radiologic findings had an F1 score of 0.9882 to 1.0. Moreover, the developed NLP system presented a higher sensitivity of potentially clinically important radiological findings than did the previous rule-based NLP system [16]. In this study, a unique NLP system was developed for extracting the clinical findings associated with LBP pain from the radiologic reports of CHA University Bundang CHA Medical Center. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Indicators** & **Recall** & **Precision** & **Accuracy** & **F1 Score** \\ \hline Listhesis & 1 & 1 & 1 & 1 \\ scoliosis & 0.9932 & 1 & 0.9944 & 0.9966 \\ Fracture & 1 & 0.986 & 0.9889 & 0.993 \\ Spondylis & 0.9903 & 1 & 0.9944 & 0.9951 \\ Annular fissure & 1 & 1 & 1 & 1 \\ Disc bulge & 1 & 1 & 1 & 1 \\ Disc degeneration & 0.9944 & 0.9944 & 0.9889 & 0.9944 \\ Disc extrusion & 1 & 1 & 1 & 1 \\ Disc herniation & 1 & 0.9889 & 0.9889 & 0.9944 \\ Disc protrusion & 1 & 1 & 1 & 1 \\ Endplate edema or Type 1 & 1 & 1 & 1 & 1 \\ Model change & & & & \\ Facet hypertrophy & 1 & 0.9611 & 0.9611 & 0.9802 \\ Central stenosis & 0.984 & 1 & 0.9889 & 0.9919 \\ Foraminal stenosis & 0.9921 & 0.9844 & 0.9833 & 0.9882 \\ Nerve root & & & & \\ displaced/compressed & & & & \\ Lateral recess stenosis & 1 & 1 & 1 & 1 \\ Spondylis & 1 & 0.9886 & 0.9889 & 0.9943 \\ Schmorf’s node & 1 & 1 & 1 & 1 \\ Osteophyte & 1 & 1 & 1 & 1 \\ Disc space narrowing & 0.9922 & 1 & 0.9944 & 0.9961 \\ Any stenosis & 1 & 1 & 1 & 1 \\ Disc sequestration & 0.9942 & 1 & 0.9944 & 0.9971 \\ Intradiscal vacuum & 0.9874 & 0.9874 & 0.9778 & 0.9874 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of the natural language processing pipeline in the test dataset (N = 180). The imaging findings associated with LBP are not explicitly coded in the medical database that is part of the electronic health record. An NLP system automatically identifies such findings from free-text radiology reports, reducing the burden of manual extraction [16]. In this study, a rule-based NLP system was developed and validated for identifying radiologic findings related to LBP in radiology reports. Radiology reports contain a substantial amount of content within electronic health records and can be used for communication and documentation of the imaging.