OBJECTIVES: Hyperthyroidism is characterized by increasing production of thyroid hormone (TH) and decreasing of thyroid stimulation hormone (TSH) secretion. The treatment of hyperthyroidism includes such as anti-thyroid drugs, radioiodine, and thyroi...
BACKGROUND: Conventional biochemical parameters may have predictive values for use in clinical identification between bipolar disorder (BD) and major depressive disorder (MDD).
Graves disease (GD) and type 2 diabetes mellitus (T2DM) both impair liver function; we therefore explored the possibility of a relationship among diabetic control, thyroid function, and liver function. This retrospective, cross-sectional study compar...
BACKGROUND: Approaching epidemiological data with flexible machine learning algorithms is of great value for understanding disease-specific association patterns. However, it can be difficult to correctly extract and understand those patterns due to t...
(GPCR)The receptor for TSH receptor (TSHR), a G protein coupled receptor (GPCR), is of particular interest as the primary antigen in autoimmune hyperthyroidism (Graves' disease) caused by stimulating TSHR antibodies. To date, only one domain of the e...
BACKGROUND: Laparoscopic tubal anastomosis (LTA) is a treatment for women who require reproduction after ligation, and there are no reliable prediction models or clinically useful tools for predicting clinical pregnancy in women who receive this proc...
Objective This study assessed the efficacy of machine learning in predicting thyrotoxicosis and hypothyroidism [thyroid-stimulating hormone >10.0 mIU/L] by leveraging age and sex as variables and integrating biochemical test parameters used by the Ja...
Hypothyroidism is a known adverse event associated with the use of immune checkpoint inhibitors (ICIs) in cancer treatment. This study aimed to develop an interpretable machine learning (ML) model for individualized prediction of hypothyroidism in pa...
Machine learning models, including thyroid biomarkers, are increasingly utilized in healthcare for biomarker prediction. These models offer the potential to enhance disease diagnosis through data-driven approaches relying on non-invasive techniques. ...