Using Machine Learning to Optimise the Danish Drowning Formula

The DDF was published in 2023. It is a text-search algorithm designed to search the unstructured text fields in databases containing electronic medical records to identify all potential water-related incidents. The DDF consists of numerous trigger words related to submersion injury (e.g., "drukn"/ drown, "vand"/water, "hav"/ocean, and "båd"/ boat). An ongoing study showed impressive performance metrics of the DDF as a drowning identification tool when applied to the Danish PEMR on unrestricted terms. However, the PPV was low for detecting drowning incidents specifically. This study aims to augment the DDF's positive predictive value and reduce the temporal demands associated with manual validation. Data are extracted from the Danish nationwide Prehospital Electronic Medical Record using the DDF and manually validated before entered into the Danish Prehospital Drowning Data (DPDD). Data from the DPDD from 2016-2021 will be split into 80% (training data) and 20% (test data) and used to train the machine learning. Data from the DPDD from 2022-2023 will be used as validation data to calculate the performance metrics for the machine learning.