Search

everythingexperts & clinicslearnfaqsresearchcommunity

for

Search:↓

In this narrative review, the authors highlighted that AI-supported electronic health record analysis could help recognize Polycystic Ovary Syndrome/Polyendocrine Metabolic Ovarian Syndrome earlier, but also warned that these systems might perpetuate historical biases if not properly validated and interpreted.

This study applied a novel Cox regression subsampling method to massive datasets, demonstrated using UK-biobank colorectal cancer data, effectively reducing computation time and memory usage while building a risk-prediction model under certain conditions involving right-censored and potentially left-truncated data with rare events.

This study reports that using machine learning models, particularly XGBoost and Random Forest, can accurately predict PCOS phenotypes based on non-invasive data, with cycle length as the most significant predictor.

In this study, researchers used Google Search ads to gather an open access dataset of 10,408 dermatological images from over 5,000 U.S. internet users, enhancing the diversity and representativeness of skin condition images available for research and artificial intelligence development.

This study found that using XGBoost with clinical and ultrasound features may provide a highly accurate, non-invasive method for diagnosing polycystic ovary syndrome, although further validation is needed to ensure robustness.