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Found 6 papers, 5 papers with code
ED-Copilot: Reduce Emergency Department Wait Time with Language Model Diagnostic Assistance
Using publicly available patient data, we collaborate with ED clinicians to curate MIMIC-ED-Assist, a benchmark that measures the ability of AI systems in suggesting laboratory tests that minimize ED wait times, while correctly predicting critical outcomes such as death.
MDI+: A Flexible Random Forest-Based Feature Importance Framework
We show that the MDI for a feature $X_k$ in each tree in an RF is equivalent to the unnormalized $R^2$ value in a linear regression of the response on the collection of decision stumps that split on $X_k$.
Synthetic Combinations: A Causal Inference Framework for Combinatorial Interventions
Our goal is to learn unit-specific potential outcomes for any combination of these $p$ interventions, i. e., $N \times 2^p$ causal parameters.
Hierarchical Shrinkage: improving the accuracy and interpretability of tree-based methods
Tree-based models such as decision trees and random forests (RF) are a cornerstone of modern machine-learning practice.
Fast Interpretable Greedy-Tree Sums
In such settings, practitioners often use highly interpretable decision tree models, but these suffer from inductive bias against additive structure.
A cautionary tale on fitting decision trees to data from additive models: generalization lower bounds
We prove a sharp squared error generalization lower bound for a large class of decision tree algorithms fitted to sparse additive models with $C^1$ component functions.
