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Q‐learning residual analysis: application to the effectiveness of sequences of antipsychotic medications for patients with schizophrenia
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/(sici)1097-0258(20000430)19:8<1011::aid-sim414>3.0.co;2-mopenalex-snapshot
Evaluating multiple treatment courses in clinical trials
inferredcites →https://doi.org/10.1002/(sici)1097-0258(20000430)19:8<1011::aid-sim414>3.3.co;2-dopenalex-snapshot
Evaluating multiple treatment courses in clinical trials
inferredcites →https://doi.org/10.1002/sim.2022openalex-snapshot
An experimental design for the development of adaptive treatment strategies
inferredcites →https://doi.org/10.1002/sim.3720openalex-snapshot
Reinforcement learning design for cancer clinical trials
inferredcites →https://doi.org/10.1002/sim.6223openalex-snapshot
A multiple imputation strategy for sequential multiple assignment randomized trials
inferredcites →https://doi.org/10.1007/978-1-4419-9076-1_11openalex-snapshot
Optimal Structural Nested Models for Optimal Sequential Decisions
inferredcites →https://doi.org/10.1007/978-1-4614-7428-9openalex-snapshot
Statistical Methods for Dynamic Treatment Regimes: Reinforcement Learning, Causal Inference, and Personalized Medicine
inferredcites →https://doi.org/10.1007/978-1-4899-2873-3openalex-snapshot
Transformation and Weighting in Regression
inferredcites →https://doi.org/10.1007/s10994-010-5229-0openalex-snapshot
Informing sequential clinical decision-making through reinforcement learning: an empirical study
inferredcites →https://doi.org/10.1016/j.drugalcdep.2006.09.008openalex-snapshot
Developing adaptive treatment strategies in substance abuse research
inferredcites →https://doi.org/10.1023/b:prev.0000037641.26017.00openalex-snapshot
A Conceptual Framework for Adaptive Preventive Interventions
inferredcites →https://doi.org/10.1037/a0029373openalex-snapshot
Q-learning: A data analysis method for constructing adaptive interventions.
inferredcites →https://doi.org/10.1080/00401706.1997.10485141openalex-snapshot
Applied Linear Statistical Models
inferredcites →https://doi.org/10.1093/biomet/asu043openalex-snapshot
Interactive model building for Q-learning
inferredcites →https://doi.org/10.1093/oxfordjournals.schbul.a006986openalex-snapshot
The National Institute of Mental Health Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) Project: Schizophrenia Trial Design and Protocol Development
inferredcites →https://doi.org/10.1093/oxfordjournals.schbul.a006989openalex-snapshot
Assessing Clinical and Functional Outcomes in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) Schizophrenia Trial
inferredcites →https://doi.org/10.1093/schbul/10.3.388openalex-snapshot
The Quality of Life Scale: An Instrument for Rating the Schizophrenic Deficit Syndrome
inferredcites →https://doi.org/10.1093/schbul/13.2.261openalex-snapshot
The Positive and Negative Syndrome Scale (PANSS) for Schizophrenia
inferredcites →https://doi.org/10.1109/tnn.1998.712192openalex-snapshot
Reinforcement Learning: An Introduction
inferredcites →https://doi.org/10.1111/1467-985x.00154openalex-snapshot
A Design for Testing Clinical Strategies: Biased Adaptive Within-Subject Randomization
inferredcites →https://doi.org/10.1111/1467-9868.00389openalex-snapshot
Optimal Dynamic Treatment Regimes
inferredcites →https://doi.org/10.1111/biom.12052openalex-snapshot
Inference for Optimal Dynamic Treatment Regimes Using an Adaptive m‐Out‐of‐n Bootstrap Scheme
inferredcites →https://doi.org/10.1111/j.1467-9469.2009.00661.xopenalex-snapshot
Estimating Optimal Dynamic Regimes: Correcting Bias under the Null
inferredcites →https://doi.org/10.1111/j.1541-0420.2009.01368.xopenalex-snapshot
Regret‐Regression for Optimal Dynamic Treatment Regimes
inferredcites →https://doi.org/10.1146/annurev-clinpsy-032511-143152openalex-snapshot
A "SMART" Design for Building Individualized Treatment Sequences
inferredcites →https://doi.org/10.1177/0962280209105013openalex-snapshot
Inference for non-regular parameters in optimal dynamic treatment regimes
inferredcites →https://doi.org/10.1177/096228029500400302openalex-snapshot
Generalized additive models for medical research
inferredcites →https://doi.org/10.1177/1740774514532570openalex-snapshot
Estimation of optimal dynamic treatment regimes
inferredcites →https://doi.org/10.1198/jasa.2010.tm10053openalex-snapshot
Adaptive Confidence Intervals for the Test Error in Classification
inferredcites →https://doi.org/10.1214/13-sts450openalex-snapshot
$\mathbf{Q}$- and $\mathbf{A}$-Learning Methods for Estimating Optimal Dynamic Treatment Regimes
inferredcites →https://doi.org/10.1214/14-ejs920openalex-snapshot
Dynamic treatment regimes: Technical challenges and applications
inferredcites →https://doi.org/10.1214/aos/1176345513openalex-snapshot
Logistic Regression Diagnostics
inferredcites →https://doi.org/10.2202/1557-4679.1173openalex-snapshot
Two-Level Stochastic Search Variable Selection in GLMs with Missing Predictors
inferredcites →https://doi.org/10.2202/1557-4679.1210openalex-snapshot
Model Checking with Residuals for g-estimation of Optimal Dynamic Treatment Regimes
inferredcites →https://doi.org/10.5705/ss.2012.196openalex-snapshot
Estimation of treatment policies based on functional predictors
inferredcites →https://doi.org/10.5705/ss.2012.364openalex-snapshot
Penalized Q-learning for dynamic treatment regimens
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4744623openalex-extracted
Reinforcement Learning Using Neural Networks in Estimating an Optimal Dynamic Treatment Regime in Patients with Sepsis: A Retrospective Cohort Study Based on the Mimic-Iii Database
inferred← citeshttps://doi.org/10.1002/9781394272587.ch9openalex-snapshot
Deep Reinforcement Learning in Healthcare and Biomedical Research
inferred← citeshttps://doi.org/10.1002/sim.8678openalex-snapshot
Ascertaining properties of weighting in the estimation of optimal treatment regimes under monotone missingness
inferred← citeshttps://doi.org/10.1002/sim.9857openalex-snapshot
Estimation and evaluation of individualized treatment rules following multiple imputation
inferred← citeshttps://doi.org/10.1016/b978-0-443-30146-9.00002-2openalex-snapshot
Advancing healthcare: Reinforcement learning applications for personalized healthcare
inferred← citeshttps://doi.org/10.1016/b978-0-443-30146-9.00008-3openalex-snapshot
Deep reinforcement learning in healthcare and biomedical application
inferred← citeshttps://doi.org/10.1016/j.artmed.2020.101964openalex-snapshot
Reinforcement learning for intelligent healthcare applications: A survey
inferred← citeshttps://doi.org/10.1016/j.artmed.2021.102137openalex-snapshot
Deep Reinforcement Learning for Fractionated Radiotherapy in Non-Small Cell Lung Carcinoma
inferred← citeshttps://doi.org/10.1016/j.chroma.2021.461900openalex-snapshot
Deep Q-learning for the selection of optimal isocratic scouting runs in liquid chromatography
inferred← citeshttps://doi.org/10.1016/j.ibmed.2024.100137openalex-snapshot
Reinforcement learning in large, structured action spaces: A simulation study of decision support for spinal cord injury rehabilitation
inferred← citeshttps://doi.org/10.1016/j.polymdegradstab.2025.111808openalex-snapshot
Prediction of mechanical properties of weathered injection-molded polycarbonate/acrylonitrile-butadiene-styrene blends using Fourier-transform infrared spectroscopy-based 1D convolutional neural network
inferred← citeshttps://doi.org/10.1016/j.tws.2025.112994openalex-snapshot
Sliding resistance of layered ultra-thin glass module for flexible cover window: Effect of mechanical properties and artificial neural network modeling
inferred← citeshttps://doi.org/10.1017/s0033291720001683openalex-snapshot
Computing schizophrenia: ethical challenges for machine learning in psychiatry
inferred← citeshttps://doi.org/10.1037/met0000232openalex-snapshot
Noninferiority and equivalence tests in sequential, multiple assignment, randomized trials (SMARTs).
inferred← citeshttps://doi.org/10.1093/biostatistics/kxy064openalex-snapshot
Power analysis in a SMART design: sample size estimation for determining the best embedded dynamic treatment regime
inferred← citeshttps://doi.org/10.1111/biom.12773openalex-snapshot
Estimation and Evaluation of Linear Individualized Treatment Rules to Guarantee Performance
inferred← citeshttps://doi.org/10.1145/3477600openalex-snapshot
Reinforcement Learning in Healthcare: A Survey
inferred← citeshttps://doi.org/10.1177/0962280217708665openalex-snapshot
Model validation and selection for personalized medicine using dynamic-weighted ordinary least squares
inferred← citeshttps://doi.org/10.1177/09622802211037066openalex-snapshot
Optimal allocation to treatments in a sequential multiple assignment randomized trial
inferred← citeshttps://doi.org/10.3390/su16051951openalex-snapshot
Predicting the Production and Depletion of Rare Earth Elements and Their Influence on Energy Sector Sustainability through the Utilization of Multilevel Linear Prediction Mixed-Effects Models with R Software
inferred← citeshttps://doi.org/10.35882/jeeemi.v4i1.1openalex-snapshot
Artificial Intelligence: A Review of Progress and Prospects in Medicine and Healthcare
inferred← citeshttps://doi.org/10.52783/jisem.v10i31s.4958openalex-snapshot
A Comprehensive Framework for Residual Analysis in Regression and Machine Learning
inferred← citeshttps://doi.org/10.5705/ss.202018.0076openalex-snapshot
Entropy Learning for Dynamic Treatment Regimes
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