This study aimed to develop a mapping table that connects nursing notes with standard terminology, focusing on nurses’ concerns for ICU patients. After extracting nursing notes from a publicly accessible database, a research team, including a nursing informatics professor and researchers with ICU experience, developed a mapping table through a four-step process: initially reviewing literature on nurses’ concerns, then extracting nursing notes from MIMIC IV and filtering the duplicate notes, subsequently defining and coding these concerns, and finally mapping them according to the CCC. Of 11,430,637 unstructured nursing notes from MIMIC IV, 265 unique notes remained after deduplication, with 208 notes reflecting nurses’ concerns and categorized into 15 groups aligned with CCC. This mapping table will be a fundamental tool for predicting clinical deterioration in ICU patients by identifying important lexicons through natural language processing.