ArticleJournal of machine learning research : JMLR2023
Semi-Supervised Off-Policy Reinforcement Learning and Value Estimation for Dynamic Treatment Regimes.
Article in Journal of machine learning research : JMLR, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
3 citing papers in PubMed.
- Federated Offline Reinforcement Learning.Journal of the American Statistical Association · 2024Article
- Semi-Supervised Off-Policy Reinforcement Learning and Value Estimation for Dynamic Treatment Regimes.Journal of machine learning research : JMLR · 2023Article
- Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices.Journal of machine learning research : JMLR · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Reinforcement learning (RL) has shown great promise in estimating dynamic treatment regimes which take into account patient heterogeneity. However, health-outcome information, used as the reward for RL methods, is often not well coded but rather embedded in clinical notes. Extracting precise outcome information is a resource-intensive task, so most of the available well-annotated cohorts are small. To address this issue, we propose a semi-supervised learning (SSL) approach that efficiently leverages a small-sized labeled data set with actual outcomes observed and a large unlabeled data set with outcome surrogates. In particular, we propose a semi-supervised, efficient approach to
Indexed as
Identifiers
41607589PMC12843220What 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.