Ignite Creation Date:
2024-05-06 @ 8:15 PM
Last Modification Date:
2024-10-26 @ 3:23 PM
Study NCT ID:
NCT06310525
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2024-03-25
First Post:
2024-03-07
Brief Title:
Using Machine Learning to Optimise the Danish Drowning Formula
Sponsor:
Prehospital Center Region Zealand
Organization:
Prehospital Center Region Zealand
Study Overview
Official Title:
Machine Learning-assisted Drowning Identification for the Danish Prehospital Drowning Data Using Machine Learning to Optimise the Danish Drowning Formula
Status:
ACTIVE_NOT_RECRUITING
Status Verified Date:
2024-03
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
DROWN_DDF2
Brief 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 eg drukn drown vandwater havocean and bad 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 DDFs 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
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 DDFs 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
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None