no code implementations • 14 Aug 2024 • Dong Neuck Lee, Michael R. Kosorok
Conventional off-policy reinforcement learning (RL) focuses on maximizing the expected return of scalar rewards.
no code implementations • 29 Jun 2024 • Sophia Yazzourh, Nicolas Savy, Philippe Saint-Pierre, Michael R. Kosorok
The goal of precision medicine is to provide individualized treatment at each stage of chronic diseases, a concept formalized by Dynamic Treatment Regimes (DTR).
no code implementations • 22 Jul 2023 • Joshua P. Zitovsky, Leslie Wilson, Michael R. Kosorok
In real-world healthcare problems, there are often multiple competing outcomes of interest, such as treatment efficacy and side effect severity.
1 code implementation • 31 Jan 2023 • Joshua P. Zitovsky, Daniel de Marchi, Rishabh Agarwal, Michael R. Kosorok
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.
no code implementations • 18 Sep 2022 • Zuyue Fu, Zhengling Qi, Zhaoran Wang, Zhuoran Yang, Yanxun Xu, Michael R. Kosorok
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.
3 code implementations • 14 Jun 2022 • Carlos García Meixide, Marcos Matabuena, Louis Abraham, Michael R. Kosorok
Survival analysis is a fundamental area of focus in biomedical research, particularly in the context of personalized medicine.
no code implementations • 26 Apr 2022 • Duyeol Lee, Helal El-Zaatari, Michael R. Kosorok, Xinyi Li, Kai Zhang
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).
1 code implementation • 19 Jul 2020 • Liangyu Zhu, Wenbin Lu, Michael R. Kosorok, Rui Song
In this article, we propose a kernel assisted learning method for estimating the optimal individualized dose rule.
no code implementations • 25 Feb 2020 • Arkopal Choudhury, Michael R. Kosorok
The distance is weighted by Mutual Information (MI) which is a measure of feature relevance between the features and the class label.
2 code implementations • 27 Jan 2020 • Yifan Cui, Michael R. Kosorok, Erik Sverdrup, Stefan Wager, Ruoqing Zhu
Forest-based methods have recently gained in popularity for non-parametric treatment effect estimation.
1 code implementation • 27 Jan 2020 • Xiaotong Jiang, Amanda E. Nelson, Rebecca J. Cleveland, Daniel P. Beavers, Todd A. Schwartz, Liubov Arbeeva, Carolina Alvarez, Leigh F. Callahan, Stephen Messier, Richard Loeser, Michael R. Kosorok
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).
no code implementations • 13 Dec 2019 • Naim U. Rashid, Daniel J. Luckett, Jingxiang Chen, Michael T. Lawson, Longshaokan Wang, Yunshu Zhang, Eric B. Laber, Yufeng Liu, Jen Jen Yeh, Donglin Zeng, Michael R. Kosorok
PDX data are characterized by correlated outcomes, a high-dimensional feature space, and a large number of treatments.
no code implementations • 8 Dec 2019 • Duyeol Lee, Kai Zhang, Michael R. Kosorok
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.
no code implementations • 5 Feb 2019 • Crystal T. Nguyen, Daniel J. Luckett, Anna R. Kahkoska, Grace E. Shearrer, Donna Spruijt-Metz, Jaimie N. Davis, Michael R. Kosorok
The field of precision medicine aims to tailor treatment based on patient-specific factors in a reproducible way.
no code implementations • 17 Jul 2018 • Daniel J. Luckett, Eric B. Laber, Samer S. El-Kamary, Cheng Fan, Ravi Jhaveri, Charles M. Perou, Fatma M. Shebl, Michael R. Kosorok
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.
no code implementations • 28 Nov 2017 • Daniel J. Luckett, Eric B. Laber, Michael R. Kosorok
Estimated composite outcomes are subsequently used to construct an estimator of an individualized treatment rule which maximizes the mean of patient-specific composite outcomes.
no code implementations • 22 Nov 2017 • Xin Zhou, Michael R. Kosorok
In this work, we propose a causal $k$-nearest neighbor method to estimate the optimal treatment regime.
no code implementations • 10 Nov 2016 • Daniel J. Luckett, Eric B. Laber, Anna R. Kahkoska, David M. Maahs, Elizabeth Mayer-Davis, Michael R. Kosorok
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.
no code implementations • 11 Jul 2014 • Qian Liu, Guanhua Chen, Michael R. Kosorok, Eric Bair
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.