Machine Learning-assisted Drowning Identification for the Danish Prehospital Drowning Data: Using Machine Learning to Optimise the Danish Drowning Formula
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 发起方
- 入组人数
- 1,500
- 试验地点
- 1
- 主要终点
- Sensitivity of the machine learning algorithm as a drowning identification tool
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •The patient must have been experiencing respiratory impairment from submersion or immersion in liquid (including persistent coughing, respiratory arrest, and unconsciousness).
- •The patient must have been in contact with the Danish prehospital Emergency Medical Services.
排除标准
- •Invalid civil registration number
结局指标
主要结局
Sensitivity of the machine learning algorithm as a drowning identification tool
时间窗: The sensitivity of the trained machine learning will be calculated based on data from 2022 and 2023.
Sensitivity \[TP / (TP+FN)\] will be calculated to show the performance of the machine learning as a drowning identification tool.
NPV of the machine learning algorithm
时间窗: The NPV of the trained machine learning will be calculated based on data from 2022 and 2023.
NPV \[TN / (FN+TN)\] will be calculated to show the machine learning test result.
PPV of the machine learning algorithm
时间窗: The PPV of the trained machine learning will be calculated based on data from 2022 and 2023.
PPV \[TP / (TP+FP)\] will be calculated to show the machine learning test result.
Specificity of the machine learning algorithm as a drowning identification tool
时间窗: The specificity of the trained machine learning will be calculated based on data from 2022 and 2023.
Specificity \[TN / (FP+TN)\] will be calculated to show the performance of the machine learning as a drowning identification tool.
次要结局
未报告次要终点
研究者
Niklas Breindahl
Principal Investigator
Region Zealand