Using Machine Learning to Optimise the Danish Drowning Formula

ACTIVE NOT RECRUITING

NCT06310525

Sponsor: Prehospital Center, Region Zealand

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.

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

Key Details

Gender

All

Age Range

Any - Any

Study Type

OBSERVATIONAL

Enrollment

1500

Start Date

2024-01-01

Completion Date

2025-12-31

Last Updated

2025-08-27

Healthy Volunteers

No

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

OTHER

Drowning incident

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

Locations (1)

Prehospital Center

Næstved, Region Sjælland, Denmark

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