[PDF] Top 20 Q- and A-learning Methods for Estimating Optimal Dynamic Treatment Regimes.
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Q- and A-learning Methods for Estimating Optimal Dynamic Treatment Regimes.
... of Q- and A-learning on a suite of test examples via Monte Carlo ...compare Q- and ...the Q-function, the propensity model, and both the Q-function and propensity ...the optimal ... See full document
114
Flexible Methods and Computation for Model Selection and Optimal Treatment Learning.
... for estimating optimal dynamic treatment ...regimes. Q-learning is based on posing a regression model to estimate the conditional expectation of the outcome at each time ... See full document
132
Flexible Statistical Learning Methods for Survival Data: Risk Prediction and Optimal Treatment Decision.
... ment regimes. Among them, Q-learning (Q for “quality”) (Watkins, 1989; Watkins and Dayan, 1992; Nahum-Shani et ...by treatment effects and interaction effects between treatments and ... See full document
100
Reinforcement Learning for Traffic Control System: Study of Exploration Methods using Q learning
... randomness. Q-learning is one of the most widely used reinforcement learning methods because of its ...the Q value function (s, a) iteration independently of the policy, and does not ... See full document
11
Approximate Dynamic Oracle for Dependency Parsing with Reinforcement Learning
... of dynamic oracles to train the parser with imitation learning methods to alleviate the ...efficient dynamic oracles have mostly been designed for arc-decomposable transition systems which are ... See full document
9
Flexible Statistical Machine Learning Methods for Optimal Treatment Decision.
... new methods have the following advantages: It uses intricate functions to learn decision rules, which, allows for model ...based Q-learning or A-learning may still lead to a more accurate and ... See full document
91
Interactive Modeling Techniques for Non-smooth Functionals in Dynamic Treatment Regimes.
... several treatment options on weight ...derive optimal decision rules for maximizing quantiles of the response ...the optimal regime changes as the target probability or quantile is ...the ... See full document
107
Optimal Dynamic Treatment Regimes from a Classification Perspective for Two Stage Studies with Survival Data.
... Weighted Learning (OWL), which also views the issue of finding the one stage optimal rule as a weighted classification ...the optimal treatment rule, but this formulation of the classification ... See full document
126
Incorporating causal factors into reinforcement learning for dynamic treatment regimes in HIV
... Structured Treatment Interruption (STI) strategies for HIV infected ...fitted Q iteration (FQI) with extremely randomized trees, was applied to learn an optimal drug prescription strategy in an ... See full document
11
List-based Interpretable Dynamic Treatment Regimes.
... every treatment regime within a pre-specified class and then take the maximizer as the estimated optimal ...given treatment and patient information and thus may be more robust to model misspecifi- ... See full document
159
Robust Statistical Method for Estimating Optimal Dynamic Treatment Regimes.
... on methods for estimating optimal treatment regimes based on data from clinical trials or observational studies, where a single decision or a series of sequential decisions may be ... See full document
90
Optimal Treatment Regimes under Constraints.
... the optimal treatment regimes of all the possible linear combinations of two competing outcomes ...on treatment efficacy, toxicity, and the risk of disease progression ...estimated ... See full document
112
Learning Rates for Q-learning
... of Q-learning algorithms and showing their dependence on the learning ...synchronous Q-learning. We show that for a polynomial learning rate we have a complexity, which is ... See full document
25
Optimal dynamic pricing and replenishment policies for deteriorating items Pages 621-630 Download PDF
... Coordination of inventory management and marketing policies play an important role in maximizing profit of firms. In this paper, we proposed an integrated model for dynamic pricing and inventory control of ... See full document
10
Modeling Approaches for Cost and Cost-Effectiveness Estimation Using Observational Data
... Propensity scores were estimated using a logistic regression model assuming correct model spec- ification according to Equation 2.21. We then applied PS covariates adjustment with normal, log- normal and gamma models, ... See full document
85
An Improved Q learning Algorithm for Path Planning of a Mobile Robot
... Machine learning is often used in mobile robots to make the robot aware about its world ...supervised learning was generally employed to train a robot to determine its next position in a given map from the ... See full document
7
Estimating a Taylor Rule with Markov Switching Regimes for Switzerland
... Hence, the observed interest rate is a weighted average of the central bank’s target interest rate and the observed interest rate from last period. The smaller U the faster i t approaches its target value i t . The error ... See full document
34
Estimation of current cumulative incidence of leukaemia-free patients and current leukaemia-free survival in chronic myeloid leukaemia in the era of modern pharmacotherapy
... disease-free patients when describing the CML patient health status. Comparing the interpretation value of these two quantities to the overall survival (OS), both advantages and disadvantages can be seen. Obviously, the ... See full document
12
Effect of different methods for estimating persistence and adherence to new glucose-lowering drugs: results of an observational, inception cohort study in Portugal
... Over the study period, a total of 327 (24.8%) patients stopped using the inception GLD: 186 (22.9%) incident and 141 (27.4%) prevalent new users. Withdrawal rates were similar ( p = 0.0591) between subgroups. The most ... See full document
12
Bayesian Methods for Optimal Treatment Allocation and Causal Inference.
... An optimal policy for spatiotemporal resource allocation ...using dynamic programming) is a massively complex mathematical function of many geographic and epidemiological inputs, requiring extensive ... See full document
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