# Using Natural Language Processing to Identify Low Back Pain in Imaging Reports
## 4 Discussion
developed the NLP algorithms for identifying the post-operative wound infection that requires reoperation after lumbar disectomy from free-text operation notes [34]. [PERSON] et al. investigated the NLP algorithms for predicting the 90-day unplanned readmission of the lumbar spine fusion patients from free-text notes during the hospitalization period [35]. [PERSON] et al. developed an NLP system based on IDEAL-X to identify venous thromboembolism from electronic medical records [36]. The free-text type clinical note, operation notes, and electronic health records could be used for developing NLP algorithms for identifying or predicting specific spinal diseases or complications of spinal surgery (Table 4) [12]. Recently, NLP system research on clinical reports written using official language has been conducted globally. [PERSON] et al. developed the NLP system for learning contextual embeddings from free-text form clinical records in France [15]. [PERSON] et al. developed an NLP system for identifying a Korean medical corpus using BERT models [37]. [PERSON] et al. developed an NLP system for classifying Norwegian pediatric CT radiology reports. A bidirectional recurrent neural network model, a convolutional neural network model, and a support vector machine model were used for training and testing the NLP system [38]. [PERSON] et al. developed an NLP system for identifying the oncologic outcomes from structured oncology reports created in the German language [39]. In previous research, machine learning- or deep learning-based NLP system predicted multiple findings using the same datasets. Moreover, the machine learning-based NLP system was more scalable than the rule-based model. However, machine learning- or deep learning-based NLP systems required larger datasets and a higher set-up cost than rule-based NLP systems [16]. In this study, the NLP system is developed using the rule-based approach. Clinicians determined and set the regular expressions using \"re.\" The various patterns of the free-text form clinical reports could be defined as regular expressions. Because rules are set by the user, and the rule-based method is easier to debug and has higher precision that what can be expected using the machine learning-based method. Several limitations of the previous studies were overcome. First, this NLP system was trained with relatively smaller datasets (\(n=720\)) than those used in previous studies. Second, the NLP system showed improved sensitivity (from 0.984 to 1), which has been pointed out as low in previous rule-based NLP systems [16]. Third, the utilization of the NLP system was extended to the CT radiology reports for extracting words associated with LBP. There are several limitations of this study. First, the radiologic findings were collected from one medical center. The pipeline may not properly process reports with different styles of reporting or expressions. In future works, numerous radiology reports with different styles of reporting should be obtained, as well as datasets from multiple institutions. Second, only the rule-based method was used for developing the NLP model. In future work, other featurization methods (e.g., N-grams, document embeddings) should be used and compared to each other. Despite these limitations, the feasibility of the NLP system to identify lumbar imaging results associated with LBP from sampled radiographic reports was confirmed. In the future, the NLP system will be used to identify lumbar imaging results among subjects without LBP, to predict whether LBP will become chronic, and to investigate the accuracy of predictions of chronic LBP persistence by investigating these predictions along with other test results, such as hematological tests. Additionally, the transformer models (i.e., BERT model) can be used for further studies. The patient datasets with LBP and without LPB will be learned in the BERT model. Through this method, we are expected to discover the sentences or words with a high correlation with LBP.