# Using Natural Language Processing to Identify Low Back Pain in Imaging Reports
## 1 Introduction  
Low back pain (LBP) is defined as pain and discomfort localized under the ribs and above the inferior glutelal folds, with or without leg pain [1]. A total of 70-80% of adults experience LBP in some form during their lives [2]. More than 85% of people under 45 years of age experience at least one LBP symptom that requires medicine or interventional treatment [3]. LBP can be classified as acute or chronic, depending on its onset. Acute LBP usually occurs suddenly, and if the pain persists for more than 3 months, it can be called chronic LBP [4]. LBP is one of the most common causes of hospital visits and the second-leading cause of sick leave [5]. Because of its high direct and indirect costs, the health, social, and economic impacts on individuals, families, and society are significant [6]. Particularly, between 5% and 10% of chronic low back pain (CLBP) cases require high costs and long-term care [7]. It is important to develop tools to help patients with recently developed back pain accurately predict whether persistent pain will occur [8]. The most common methods to evaluate chronic LBP are X-radiation (X-ray) examinations, computed tomography (CT), and magnetic resonance imaging (MRI) [9]. Theradiological findings identified from reviewing those images are translated by clinicians and transferred to a radiology report. A radiology report is a formal interpretation of a radiological examination and contains radiological findings related to the clinical diagnosis and treatment decisions [10; 11]. However, many radiologic reports use a free-text form language and remain unstructured, requiring a manual review to harvest clinical information. Manual extraction of information is labor-intensive and impractical for large-scale research. It is important to create a pipeline for extracting clinical information from the radiology reports. Natural language processing (NLP) is defined as understanding, analyzing, and extracting meaningful information from text (natural language) by computer science [12]. NLP, a computer technology specializing in text processing, can be used to extract critical information from unstructured text in electronic health records [13]. The NLP system is classified into three approaches: the rule-based approach, the machine learning-based approach, and the hybrid types approach [12]. For example, a Bidirectional Encoder Representations from Transformers (BERT)-based NLP pipeline (a state-of-the-art deep learning model for language processing) was used to identify important findings in intensive care chest radiograph reports [14]. [PERSON] et al. used the NLP system developed by the BERT model. The free-text form records in the University Hospital of Amiens-Picardy in France was used to develop the BERT model. This BERT model learned contextual embeddings. In this research, the utilization of the BERT method in the medical care field was discussed [15]. [PERSON] et al. developed a rule-based NLP system and a machine learning-based NLP system to identify lumbar spine imaging findings related to LBP and compared their performance [16]. In previous studies, there were some points that need to be improved. First, while the machine learning approach has been actively studied, the rule-based approach has not been studied much in the NLP system for extracting words associated with LBP. The set-up cost of the rule-based approach is lower than that of the machine learning approach. The training dataset size of the rule-based approach is also smaller than that of the machine learning approach. In previous studies, the rule-based approach presented high specificity, but moderate sensitivity [16]. This study developed an improved rule-based NLP system to that used in the previous research. Second, there is a need to develop the NLP system for extracting words associated with LBP in CT radiology reports. In previous studies, the NLP system was not trained and tested in the CT radiology reports [16; 17; 18; 19; 20; 21].