The concept of developing an automated system for managing the trajectories of oil and gas wells through the use of modem information technologies is examined. A three-level algorithmic approach is proposed for making control decisions based on a predictive model of drilling equipment movement. An approach to constructing a multi-mode predictive model of well trajectory is presented, which serves as the mathematical foundation of an information-analytical subsystem aimed at solving real-time trajectory management tasks.

The article explores approaches based on machine learning for the prevention of endocrine diseases in medicine, highlighting their advantages and prospects for application. The possibility of predicting early stages of diseases using data collection, analysis, and machine learning algorithms is examined.