Natural Language Processing for Scoliosis Research: Automated Extraction of Patient Outcomes from Clinical Notes

Clinical notes of scoliosis contain essential yet unstructured data that are vital for both research and clinical decision-making. Key outcomes, such as Cobb angle progression, bracing compliance, and surgical results, are often buried in free-text formats, making manual extraction labor-intensive and error-prone. To address this challenge, we developed an automated Natural Language Processing (NLP) pipeline specifically designed to extract scoliosis-related clinical information. The system integrates techniques, such as Named Entity Recognition (NER) and relation extraction, to accurately identify and structure relevant entities and their relationships. The evaluation was conducted using a gold-standard dataset, yielding a high performance with a precision of 93%, recall of 91%, and an F1score of 92% in extracting key clinical entities. These results demonstrate the effectiveness of the system for transforming unstructured notes into structured and usable data. This advancement supports scalable data analysis and predictive modeling in scoliosis research. Future studies will focus on real-time clinical deployment and adaptation to other medical domains.