Improving triage performance in emergency departments using machine learning and natural language processing: a systematic review

NLP methods improved ML algorithms' classification capability using triage nursing and medical notes and structured clinical data compared to algorithms using only structured data. Feature engineering (FE) and class imbalance correction methods enhanced ML workflows' performance, but FE and eXplainable Artificial Intelligence (XAI) were underexplored in this field. Registration and funding. This systematic review has been registered (registration number: CRD42024604529) in the International Prospective Register of Systematic Reviews (PROSPERO) and can be accessed online at the following URL: https: //www. crd. york. ac. uk/prospero/displayᵣecord. php? RecordID=604529. Funding for this work was provided by the National Council for Scientific and Technological Development (CNPq), Brazil.