The Danish Drowning Formula (DDF) was designed to search the unstructured text fields in the Danish nationwide Prehospital Electronic Medical Record on unrestricted terms with comprehensive search criteria to identify all potential water-related incidents and achieve a high sensitivity. This was important as drowning is a rare occurrence, but it resulted in a low Positive Predictive Value for detecting drowning incidents specifically. This study aims to augment the positive predictive value of the DDF and reduce the temporal demands associated with manual validation.
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AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
Sensitivity of the machine learning algorithm as a drowning identification tool
Timeframe: The sensitivity of the trained machine learning will be calculated based on data from 2022 and 2023.
Specificity of the machine learning algorithm as a drowning identification tool
Timeframe: The specificity of the trained machine learning will be calculated based on data from 2022 and 2023.
PPV of the machine learning algorithm
Timeframe: The PPV of the trained machine learning will be calculated based on data from 2022 and 2023.
NPV of the machine learning algorithm
Timeframe: The NPV of the trained machine learning will be calculated based on data from 2022 and 2023.