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Found 6 papers, 3 papers with code
Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification
Most of the current methods of subgroup analysis begin with a particular algorithm for estimating individualized treatment effects (ITE) and identify subgroups by maximizing the difference across subgroups of the average treatment effect in each subgroup.
Learning outside the Black-Box: The pursuit of interpretable models
Machine Learning has proved its ability to produce accurate models but the deployment of these models outside the machine learning community has been hindered by the difficulties of interpreting these models.
Optimal Piecewise Local-Linear Approximations
Piecewise local-linear models provide a natural way to extend local-linear models to explain the global behavior of the model.
DPSCREEN: Dynamic Personalized Screening
However, there has been limited work to address the personalized screening for these different diseases.
AutoCP: Automated Pipelines for Accurate Prediction Intervals
Successful application of machine learning models to real-world prediction problems, e. g. financial forecasting and personalized medicine, has proved to be challenging, because such settings require limiting and quantifying the uncertainty in the model predictions, i. e. providing valid and accurate prediction intervals.
SDF-Bayes: Cautious Optimism in Safe Dose-Finding Clinical Trials with Drug Combinations and Heterogeneous Patient Groups
Phase I clinical trials are designed to test the safety (non-toxicity) of drugs and find the maximum tolerated dose (MTD).
