Prediction of urban medical emergencies using machine learning models based on spatial and temporal variables

The sliding window methodology effectively captures periods with higher emergency occurrences, potentially contributing to potential injury prevention strategies. Although fine-grained prediction accuracy (specific time and location) is limited, the model performs well across an approximately 165 km² urban area. Though the model's fine-grained accuracy in predicting specific times and locations of emergencies is limited, the automated prediction model demonstrates a promising ability to identify periods with increased emergency occurrences when aggregating spatial information. Further use of fine-grained data and recent machine learning techniques may improve its precision, particularly at smaller spatial and temporal scales.