… 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 …

… 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 …

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

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

… 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 …

… algorithm contains different models for each stage. For the first stage, we developed different
fine-tuned Machine Learning (… Our algorithm was compared to a baseline VM consolidation …

The individualized treatment recommendation (ITR) is an important analytic framework for …
From the machine learning perspective, the solution to the ITR problem can be formulated as …

… Considering the variability of confounding factors, a two-level algorithm was introduced
in our model for DET prediction to achieve a satisfying predictive model. Furthermore, unlike …

… to individualize … algorithm for a response to methotrexate based on sociodemographic,
clinical, serologic, and genomic data from patients with early RA using machine learning (ML) …

… Patient factors can be used by the algorithm to provide predicted individualized scores of
future functional status after the initial insult. Using these predictions, which are based on a …