Search Results for author:
Found 7 papers, 3 papers with code
Towards Outcome-Driven Patient Subgroups: A Machine Learning Analysis Across Six Depression Treatment Studies
We analyzed data from six clinical trials of pharmacological treatment for depression (total n = 5438) using the Differential Prototypes Neural Network (DPNN), a neural network model that derives patient prototypes which can be used to derive treatment-relevant patient clusters while learning to generate probabilities for differential treatment response.
Better Experimental Design by Hybridizing Binary Matching with Imbalance Optimization
This rate benefits from the greedy switching heuristic which is $O_p(n^{-3})$ and the rate of matching which is $O_p(n^{-1})$.
Methodology
Inference for the Effectiveness of Personalized Medicine with Software
We demonstrate our method's promise on simulated data as well as on data from a randomized trial investigating two treatments for depression.
Methodology
bartMachine: Machine Learning with Bayesian Additive Regression Trees
We present a new package in R implementing Bayesian additive regression trees (BART).
Peeking Inside the Black Box: Visualizing Statistical Learning with Plots of Individual Conditional Expectation
This article presents Individual Conditional Expectation (ICE) plots, a tool for visualizing the model estimated by any supervised learning algorithm.
Applications
Prediction with Missing Data via Bayesian Additive Regression Trees
We present a method for incorporating missing data in non-parametric statistical learning without the need for imputation.
