Machine Learning Approaches to Identify Factors Associated with Women's Vasomotor Symptoms Using General Hospital Data.
Ki Jin RyuKyong Wook YiYong Jin KimJung-Ho ShinJun Young HurTak KimJong Bae SeoKwang-Sig LeeHyuntae ParkPublished in: Journal of Korean medical science (2021)
Machine learning provides an invaluable decision support system for the prediction of VMS. For managing VMS, comprehensive consideration is needed regarding thyroid function, lipid profile, liver function, inflammation markers, insulin resistance, monocyte count, cancer antigens, and lung function.
Keyphrases
- machine learning
- lung function
- insulin resistance
- polycystic ovary syndrome
- big data
- cystic fibrosis
- chronic obstructive pulmonary disease
- air pollution
- dendritic cells
- artificial intelligence
- oxidative stress
- peripheral blood
- adipose tissue
- type diabetes
- electronic health record
- metabolic syndrome
- deep learning
- high fat diet
- pregnancy outcomes
- endothelial cells
- depressive symptoms
- physical activity
- emergency department
- adverse drug
- high fat diet induced