Lung Neoplasms
Conditions
Keywords
Environmental Substance
Brief summary
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.
Detailed description
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.
Interventions
None listed
Sponsors
Pei-Hung Liao
Study design
Observational model
CASE_CONTROL
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
20 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
1. 20 years or older; 2. conscious, without mental disorders; 3. able to use Mandarin to communicate, had a smartphone or tablet computer and could operate it by themselves, 4. and were willing to participate in the study after the reasons for participation were explained and a consent form was signed, indicating their consent.
Exclusion criteria
* The
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| identify factors that may lead to lung cancer and environmental hormones. | 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 |
Countries
Taiwan
Outcome results
None listed