NCT06310525

Using Machine Learning to Optimise the Danish Drowning Formula

Machine Learning-assisted Drowning Identification for the Danish Prehospital Drowning Data: Using Machine Learning to Optimise the Danish Drowning Formula

• Status: Active Not Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 1,500 (estimated)
• Sponsor: Prehospital Center, Region Zealand · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: Not accepted

Summary

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.

Detailed description

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.

Conditions

• Drowning
• Drowning and Submersion While in Bath-Tub
• Drowning and Submersion While in Natural Water
• Drowning and Submersion While in Swimming-Pool
• Drowning and Submersion Due to Fall Off Ship
• Drowning and Nonfatal Submersion
• Drowning, Near
• Drowning; Asphyxia

Interventions

Type	Name	Description
OTHER	Drowning incident	Drowning was defined by the WHO in 2002 as "the process of experiencing respiratory impairment from submersion or immersion in liquid".

Timeline

• Start date: 2024-01-01
• Primary completion: 2025-12-31
• Completion: 2025-12-31
• First posted: 2024-03-15
• Last updated: 2025-08-27

Locations

1 site across 1 country: Denmark