Identifying the Risk of Lung Cancer Linked to Environmental Hormones
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 发起方
- 入组人数
- 128
- 试验地点
- 2
- 主要终点
- identify factors that may lead to lung cancer and environmental hormones.
研究概览
简要总结
To apply machine learning to construct an association model regarding lung cancer and environmental hormones to more comprehensively identify factors that may lead to lung cancer and to improve existing lung cancer nursing assessments.
详细描述
The present study is exploratory, and its design was divided into three stages: Stage 1 explored the environmental hormonal risk factors for lung cancer and constructed an association model using machine learning algorithms. Stage 2 validated the results of the association model with better predictive results in clinical settings. Stage 3 involved recommendations for reconstructing nursing assessments based on the results of the clinical model validation. The three stages are summarized as follows.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Control
- 时间视角
- Cross Sectional
入排标准
- 年龄范围
- 20 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •20 years or older;
- •conscious, without mental disorders;
- •able to use Mandarin to communicate, had a smartphone or tablet computer and could operate it by themselves,
- •and were willing to participate in the study after the reasons for participation were explained and a consent form was signed, indicating their consent.
排除标准
- •The exclusion criteria were those who could not understand the content of the nursing assessment questions and those with mental disorders.
结局指标
主要结局
identify factors that may lead to lung cancer and environmental hormones.
时间窗: the database research samples from May 2014 to December 2020.
This association model can assist in identifying the risk groups of lung cancer and advising physicians on the appropriate environmental hormone testing and treatment to provide nursing guidance and medical advice
次要结局
未报告次要终点
研究者
Pei-Hung Liao
Associate Professor
National Taipei University of Nursing and Health Sciences