Dosing Model

Dosing models aim to optimize medication administration by predicting individual patient responses and adapting dosages accordingly. Current research heavily utilizes machine learning, particularly deep reinforcement learning and contextual bandit algorithms, often coupled with pharmacokinetic/pharmacodynamic (PK/PD) models to improve prediction accuracy and personalize treatment. This work addresses challenges like delayed drug effects, limited data, and the need for explainable models, focusing on applications such as anticoagulant (warfarin, heparin) and immunosuppressant (tacrolimus) dosing. Improved dosing models promise safer and more effective therapies, leading to better patient outcomes and potentially reducing healthcare costs.

Papers

November 8, 2024

MIPD: A Multi-sensory Interactive Perception Dataset for Embodied Intelligent Driving
Zhiwei Li, Tingzhen Zhang, Meihua Zhou, Dandan Tang, Pengwei Zhang, Wenzhuo Liu, Qiaoning Yang, Tianyu Shen, Kunfeng Wang, Huaping Liu
Autonomous Driving Perception Datasets Intelligent Driving Embodied Vision Multisensory Data Dosing Model

September 10, 2024

Deep kernel representations of latent space features for low-dose PET-MR imaging robust to variable dose reduction
Cameron Dennis Pain, Yasmeen George, Alex Fornito, Gary Egan, Zhaolin Chen
Deep Kernel Dose Positron Emission Tomography Dose Map Dosing Model

April 26, 2024

An Explainable Deep Reinforcement Learning Model for Warfarin Maintenance Dosing Using Policy Distillation and Action Forging
Sadjad Anzabi Zadeh, W. Nick Street, Barrett W. Thomas
Deep Reinforcement Learning Proximal Policy Optimization Action Feature Policy Distillation Dosing Model Explainable Deep Reinforcement Learning Warfarin Dosing

February 16, 2024

Optimizing Warfarin Dosing Using Contextual Bandit: An Offline Policy Learning and Evaluation Method
Yong Huang, Charles A. Downs, Amir M. Rahmani
Reinforcement Learning Evaluation Method Based Policy Offline Policy Learning Contextual Bandit Setting Dosing Model Warfarin Dosing

June 24, 2023

Individualized Dosing Dynamics via Neural Eigen Decomposition
Stav Belogolovsky, Ido Greenberg, Danny Eytan, Shie Mannor
Differential Equation Neural Stochastic Differential Equation Dosing Model Neural Eigenfunctions

May 31, 2023

Reliable Off-Policy Learning for Dosage Combinations
Jonas Schweisthal, Dennis Frauen, Valentyn Melnychuk, Stefan Feuerriegel
Dose Response Dosing Model

April 19, 2023

Model Based Reinforcement Learning for Personalized Heparin Dosing
Qinyang He, Yonatan Mintz
Model Based Reinforcement Learning Sequential Decision Making Scenario Generation Dosing Model

January 2, 2023

On the Challenges of using Reinforcement Learning in Precision Drug Dosing: Delay and Prolongedness of Action Effects
Sumana Basu, Marc-André Legault, Adriana Romero-Soriano, Doina Precup
Reinforcement Learning Reward Function Observable Markov Decision Process Long Span C POMDPs Account Time Delay Dosing Model

May 9, 2022

Predicting tacrolimus exposure in kidney transplanted patients using machine learning
Andrea M. Storås, Anders Åsberg, Pål Halvorsen, Michael A. Riegler, Inga Strümke
Machine Learning Patient Friendly Language Canary Exposure Kidney Anatomy Pharmacokinetic Parameter Dosing Model Clopidogrel Treatment Failure

February 7, 2022

Optimizing Warfarin Dosing using Deep Reinforcement Learning
Sadjad Anzabi Zadeh, W. Nick Street, Barrett W. Thomas
Deep Reinforcement Learning Dosing Model Warfarin Dosing

Dosing Model

Papers

MIPD: A Multi-sensory Interactive Perception Dataset for Embodied Intelligent Driving

Deep kernel representations of latent space features for low-dose PET-MR imaging robust to variable dose reduction

An Explainable Deep Reinforcement Learning Model for Warfarin Maintenance Dosing Using Policy Distillation and Action Forging

Optimizing Warfarin Dosing Using Contextual Bandit: An Offline Policy Learning and Evaluation Method

Individualized Dosing Dynamics via Neural Eigen Decomposition

Reliable Off-Policy Learning for Dosage Combinations

Model Based Reinforcement Learning for Personalized Heparin Dosing

On the Challenges of using Reinforcement Learning in Precision Drug Dosing: Delay and Prolongedness of Action Effects

Predicting tacrolimus exposure in kidney transplanted patients using machine learning

Optimizing Warfarin Dosing using Deep Reinforcement Learning