… one of the biggest cohorts analyzed by machine learning approaches [22]. Furthermore, we
… In conclusion, we have developed and validated using a machine learning driven approach …

… This systematic review aims to summarize the status of ML applications for dose individualization
by profiling the study design, ML algorithms, features, and performance validation with …

… only six machine learning regression algorithms that have been commonly used to date in
neuroimaging filed. Obviously, there are a number of widely used regression algorithms out …

… of machine learning algorithms with useful statistical guarantees, we argue instead for modeling
the individualized … against ITE-estimation using machine learning, and describe how the …

… training machine learning methods to estimate individualized … ‐of‐the‐art machine learning
methods for causal inference, … and machine learning for making individualized treatment …

… ML is a powerful tool that can provide individualized patient risk profiles by more … ML
algorithms trained on readily available perioperative clinical data can provide an individualized risk …

… machine learning in the field of assessing the efficacy of PAC treatment. We hypothesized
that the multivariate data analysis utilizing machine learning … and treatment algorithms tailored …

… treatment rule based on an ensemble machine learning … suggest that a clinically useful
individualized treatment rule for … , 38 we used diverse candidate algorithms to optimize Super …

… individualized variants of PATE, namely upsampling and weighting. Each variant modifies
the original PATE algorithm in some aspects to provide individualized … concrete algorithms …

… algorithm was the best performing algorithm (… of machine learning could become an
invaluable asset in the process of automated personalized coaching. The individualized algorithms …