Developing a clinical decision support tool for stratifying stroke risk in patients presenting with dizziness to the emergency department: A retrospective cohort study.

Introduction: Acute dizziness accounts for approximately 4% of emergency department (ED) visits, with stroke often missed. Current methods for stroke detection in dizzy patients have notable limitations, with vestibular strokes missed in a substantial proportion of ED visits. This study aimed to develop a machine learning (ML) tool to assess stroke risk in patients with acute dizziness. Methods: We developed an ensemble model combining four ML algorithms using structured electronic medical record data and unstructured ED physician notes. Model performance was evaluated on a holdout test set and compared with the ABCD Results: The ensemble model achieved the highest AUC at 0.880, significantly outperforming the ABCD Conclusions: Our ensemble prediction model effectively stratifies stroke risk in ED patients with acute dizziness. By integrating natural language processing of clinical notes with structured patient data, the model offers a more accurate risk assessment than traditional methods. The implementation of this tool could improve patient outcomes by directing advanced neuroimaging to high-risk patients while avoiding unnecessary testing in low-risk patients, ultimately enhancing patient safety and optimizing resource utilization.