ArticleHealthcare informatics : the business magazine for information and communication systems2017
Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Data.
Article in Healthcare informatics : the business magazine for information and communication systems, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A Systematic Review of Machine Learning Techniques in Hematopoietic Stem Cell Transplantation (HSCT).Sensors (Basel, Switzerland) · 2020Pooled it
- Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2018Pooled it
- Smart Imitator: Learning from Imperfect Clinical Decisions.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Artificial Intelligence in Audiology: A Scoping Review of Current Applications and Future Directions.Sensors (Basel, Switzerland) · 2024Article
- PrescDRL: deep reinforcement learning for herbal prescription planning in treatment of chronic diseases.Chinese medicine · 2024Article
- Mathematical Model-Driven Deep Learning Enables Personalized Adaptive Therapy.Cancer research · 2024Article
- Optimizing warfarin dosing for patients with atrial fibrillation using machine learning.Scientific reports · 2024Article
- Enhancing Medical Training Through Learning From Mistakes by Interacting With an Ill-Trained Reinforcement Learning Agent.IEEE transactions on learning technologies · 2024Article
- Applications of Artificial Intelligence in Temporal Bone Imaging: Advances and Future Challenges.Cureus · 2023Review
- Learning dynamic treatment strategies for coronary heart diseases by artificial intelligence: real-world data-driven study.BMC medical informatics and decision making · 2022Article
- Reinforcement Learning for Precision Oncology.Cancers · 2021Review
- Individualized resuscitation strategy for septic shock formalized by finite mixture modeling and dynamic treatment regimen.Critical care (London, England) · 2021Article
- A scoping review of studies using observational data to optimise dynamic treatment regimens.BMC medical research methodology · 2021Article
- Reinforcement Learning in Neurocritical and Neurosurgical Care: Principles and Possible Applications.Computational and mathematical methods in medicine · 2021Review
- Representation Learning for Integrating Multi-domain Outcomes to Optimize Individualized Treatments.Advances in neural information processing systems · 2020Article
- Precision medicine: Statistical methods for estimating adaptive treatment strategies.Bone marrow transplantation · 2020Article
- Article
- Artificial Intelligence and the Implementation Challenge.Journal of medical Internet research · 2019Article
- Deep Reinforcement Learning in Medicine.Kidney diseases (Basel, Switzerland) · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
In this paper, we propose the first deep reinforcement learning framework to estimate the optimal Dynamic Treatment Regimes from observational medical data. This framework is more flexible and adaptive for high dimensional action and state spaces than existing reinforcement learning methods to model real life complexity in heterogeneous disease progression and treatment choices, with the goal to provide doctor and patients the data-driven personalized decision recommendations. The proposed deep reinforcement learning framework contains a supervised learning step to predict the most possible expert actions; and a deep reinforcement learning step to estimate the long term value function of Dynamic Treatment Regimes. We motivated and implemented the proposed framework on a data set from the Center for International Bone Marrow Transplant Research (CIBMTR) registry database, focusing on the sequence of prevention and treatments for acute and chronic graft versus host disease. We showed results of the initial implementation that demonstrates promising accuracy in predicting human expert decisions and initial implementation for the reinforcement learning step.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.