Optimal Treatment

Optimal treatment research focuses on developing data-driven methods to personalize treatment strategies, maximizing patient outcomes while ensuring safety. Current efforts concentrate on reinforcement learning (RL) algorithms, including model-based and off-policy approaches like Conservative Q-Learning, often enhanced by techniques such as inverse reinforcement learning and attention mechanisms to handle complex scenarios and limited data. These advancements aim to improve decision-making in various medical domains, from medication dosing to chronic disease management, by providing more effective and interpretable treatment recommendations based on individual patient characteristics and treatment history. The ultimate goal is to translate these research findings into improved clinical practice and better patient care.

Papers

September 20, 2024

OMG-RL:Offline Model-based Guided Reward Learning for Heparin Treatment
Yooseok Lim, Sujee Lee
Reinforcement Learning Model Based Offline Reinforcement Learning Optimal Treatment Reward Function Parameter Complex Reward Function Warfarin Dosing

July 23, 2024

MiranDa: Mimicking the Learning Processes of Human Doctors to Achieve Causal Inference for Medication Recommendation
Ziheng Wang, Xinhe Li, Haruki Momma, Ryoichi Nagatomi
Causal Inference Civil Engineering Phase Internal Medicine DOCTOR Medication Recommendation Learning Process Optimal Treatment Victim Model

October 30, 2023

Stage-Aware Learning for Dynamic Treatments
Hanwen Ye, Wenzhuo Zhou, Ruoqing Zhu, Annie Qu
Multi Stage Dynamic Treatment Regime Optimal Treatment Optimal Regime

October 23, 2023

March 8, 2023

Inference on Optimal Dynamic Policies via Softmax Approximation
Qizhao Chen, Morgane Austern, Vasilis Syrgkanis
Scientific Inference Optimal Policy Softmax Function Dynamic Treatment Regime Optimal Treatment Optimal Regime

February 15, 2023

Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applications
Milashini Nambiar, Supriyo Ghosh, Priscilla Ong, Yu En Chan, Yong Mong Bee, Pavitra Krishnaswamy
Reinforcement Learning Policy Reinforcement Learning Conservative Q Learning Double Deep Q Network Optimal Treatment

October 13, 2022

Fast Optimization of Weighted Sparse Decision Trees for use in Optimal Treatment Regimes and Optimal Policy Design
Ali Behrouz, Mathias Lecuyer, Cynthia Rudin, Margo Seltzer
Decision Tree Optimal Policy Greater Public Use Inverse Propensity Weighting Efficient Sparse Optimal Treatment Fast Optimization Sparse Decision Tree

July 17, 2022

Reinforcement Learning For Survival, A Clinically Motivated Method For Critically Ill Patients
Thesath Nanayakkara
Reinforcement Learning Deep RL Intensive Care Deep Survival Reinforcement Learning Objective Optimal Treatment

June 22, 2022

Learning Optimal Treatment Strategies for Sepsis Using Offline Reinforcement Learning in Continuous Space
Zeyu Wang, Huiying Zhao, Peng Ren, Yuxi Zhou, Ming Sheng
Deep Reinforcement Learning Offline Reinforcement Learning Real Time Continuous Space Dynamic Treatment Regime Standard Sepsis Optimal Treatment Medical Decision

December 8, 2021

Ambiguous Dynamic Treatment Regimes: A Reinforcement Learning Approach
Soroush Saghafian
Unobserved Confounders Underlying Causal Dynamic Treatment Regime Optimal Treatment

November 26, 2021

ESCADA: Efficient Safety and Context Aware Dose Allocation for Precision Medicine
Ilker Demirel, Ahmet Alparslan Celik, Cem Tekin
Dose Prediction Personalized Treatment Recommendation Optimal Treatment Precision Medicine Safety Argument

November 5, 2021

Interpretable Personalized Experimentation
Han Wu, Sarah Tan, Weiwei Li, Mia Garrard, Adam Obeng, Drew Dimmery, Shaun Singh, Hanson Wang, Daniel Jiang, Eytan Bakshy
Heterogeneous Treatment Effect Optimal Treatment Online Policy Customization Personalized Experimentation

Optimal Treatment

Papers

OMG-RL:Offline Model-based Guided Reward Learning for Heparin Treatment

MiranDa: Mimicking the Learning Processes of Human Doctors to Achieve Causal Inference for Medication Recommendation

Stage-Aware Learning for Dynamic Treatments

Safe and Interpretable Estimation of Optimal Treatment Regimes

Reinforcement learning in large, structured action spaces: A simulation study of decision support for spinal cord injury rehabilitation

Inference on Optimal Dynamic Policies via Softmax Approximation

Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applications

Fast Optimization of Weighted Sparse Decision Trees for use in Optimal Treatment Regimes and Optimal Policy Design

Reinforcement Learning For Survival, A Clinically Motivated Method For Critically Ill Patients

Learning Optimal Treatment Strategies for Sepsis Using Offline Reinforcement Learning in Continuous Space

Ambiguous Dynamic Treatment Regimes: A Reinforcement Learning Approach

ESCADA: Efficient Safety and Context Aware Dose Allocation for Precision Medicine

Interpretable Personalized Experimentation