The accurate prediction of Gallium Nitride High-Electron Mobility Transistors (GaN HEMTs) lifetime is essential for ensuring the reliability of power electronics. However, the complex and often competing degradation mechanisms within a single GaN-based transistor make lifetime extrapolation particularly challenging, especially under limited-data scenarios. In this work, we explore two machine learning approaches, i.e., XGBoost Regression and Gaussian Process Regression (GPR), for static gate lifetime prediction based on early measurements of current and ON-state resistance. In particular, we use features derived from empirical models to improve accuracy and model-specific methods to estimate uncertainty. We compare bootstrapped XGBoost ensembles, which yield empirical confidence intervals, with GPR, which provides analytical uncertainty estimates. Experiments on a time-dependent gate breakdown (TDGB) dataset spanning 16 voltage–temperature combinations show that GPR achieves an SMAPE of 8.8% and ECE of 0.028, outperforming XGBoost in Leave-One-Condition-Out Cross-Validation. These results highlight the feasibility of our proposed uncertainty-aware gate-lifetime prediction for Schottky p-GaN gate HEMTs in small-sample settings, and provide a basis for extending the framework towards time-dependent degradation modeling.

Post-induction hypotension (PIH) occurs shortly after anesthesia induction and is related to several post-operative complications. Medications delivered during induction and maintenance of anesthesia are significantly related to PIH occurrence, which remains common due to the intricate nature of clinical factors. To enhance decision-making on anesthestic dosing, machine learning (ML) is proposed to predict the risk of PIH associated with specific anesthetic dosages. This study focuses on the development of a prediction model for PIH to support anesthesia decision-making. Trained on 320 cases from the VitalDB database, the model incorporates demographic data, vital signs, and medication dosing information. By including the dosage of propofol administered during the induction period as an input variable, the algorithm predicts PIH risk before induction, providing valuable insights into the safety of propofol dosage plans. The results were validated using nested cross-validation, achieving high performance (precision of 0.83 and recall of 0.84). Moreover, an advisory model demonstrates the potential for personalizing a safe propofol anesthetics range for an individual patient.