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Found 17 papers, 4 papers with code
A Flexible Framework for Incorporating Patient Preferences Into Q-Learning
In real-world healthcare problems, there are often multiple competing outcomes of interest, such as treatment efficacy and side effect severity.
Revisiting Bellman Errors for Offline Model Selection
Offline model selection (OMS), that is, choosing the best policy from a set of many policies given only logged data, is crucial for applying offline RL in real-world settings.
Offline Reinforcement Learning with Instrumental Variables in Confounded Markov Decision Processes
Due to the lack of online interaction with the environment, offline RL is facing the following two significant challenges: (i) the agent may be confounded by the unobserved state variables; (ii) the offline data collected a prior does not provide sufficient coverage for the environment.
Neural interval-censored Cox regression with feature selection
The classical Cox model emerged in 1972 promoting breakthroughs in how patient prognosis is quantified using time-to-event analysis in biomedicine.
Discussion of Multiscale Fisher's Independence Test for Multivariate Dependence
In this comment, we would like to discuss a general framework unifying the MULTIFIT and other tests and compare it with the binary expansion randomized ensemble test (BERET hereafter) proposed by Lee et al. (In press).
Kernel Assisted Learning for Personalized Dose Finding
In this article, we propose a kernel assisted learning method for estimating the optimal individualized dose rule.
Missing Data Imputation for Classification Problems
The distance is weighted by Mutual Information (MI) which is a measure of feature relevance between the features and the class label.
Estimating heterogeneous treatment effects with right-censored data via causal survival forests
Forest-based methods have recently gained in popularity for non-parametric treatment effect estimation.
Technical Background for "A Precision Medicine Approach to Develop and Internally Validate Optimal Exercise and Weight Loss Treatments for Overweight and Obese Adults with Knee Osteoarthritis"
We provide additional statistical background for the methodology developed in the clinical analysis of knee osteoarthritis in "A Precision Medicine Approach to Develop and Internally Validate Optimal Exercise and Weight Loss Treatments for Overweight and Obese Adults with Knee Osteoarthritis" (Jiang et al. 2020).
High dimensional precision medicine from patient-derived xenografts
PDX data are characterized by correlated outcomes, a high-dimensional feature space, and a large number of treatments.
The Binary Expansion Randomized Ensemble Test (BERET)
Recently, the binary expansion testing framework was introduced to test the independence of two continuous random variables by utilizing symmetry statistics that are complete sufficient statistics for dependence.
Estimating Individualized Treatment Regimes from Crossover Designs
The field of precision medicine aims to tailor treatment based on patient-specific factors in a reproducible way.
Receiver Operating Characteristic Curves and Confidence Bands for Support Vector Machines
We propose a method for constructing confidence bands for the SVM ROC curve and provide the theoretical justification for the SVM ROC curve by showing that the risk function of the estimated decision rule is uniformly consistent across the weight parameter.
Estimation and Optimization of Composite Outcomes
Estimated composite outcomes are subsequently used to construct an estimator of an individualized treatment rule which maximizes the mean of patient-specific composite outcomes.
Causal nearest neighbor rules for optimal treatment regimes
In this work, we propose a causal $k$-nearest neighbor method to estimate the optimal treatment regime.
Estimating Dynamic Treatment Regimes in Mobile Health Using V-learning
However, existing methods for estimating optimal dynamic treatment regimes are designed for a small number of fixed decision points occurring on a coarse time-scale.
Biclustering Via Sparse Clustering
This framework can be used to identify biclusters that differ with respect to the means of the features, the variance of the features, or more general differences.
