Article: A Contextual-Bandit-Based Approach for Informed Decision-Making in Clinical Trials.
2022 Volume 12, Issue 8
Abstract: Clinical trials are conducted to evaluate the efficacy of new treatments. Clinical trials involving multiple treatments utilize the randomization of treatment assignments to enable the evaluation of treatment efficacies in an unbiased manner. Such ... ...
Abstract | Clinical trials are conducted to evaluate the efficacy of new treatments. Clinical trials involving multiple treatments utilize the randomization of treatment assignments to enable the evaluation of treatment efficacies in an unbiased manner. Such evaluation is performed in post hoc studies that usually use supervised-learning methods that rely on large amounts of data collected in a randomized fashion. That approach often proves to be suboptimal in that some participants may suffer and even die as a result of having not received the most appropriate treatments during the trial. Reinforcement-learning methods improve the situation by making it possible to learn the treatment efficacies dynamically during the course of the trial, and to adapt treatment assignments accordingly. Recent efforts using |
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Language | English |
Publishing date | 2022-08-21 |
Publishing country | Switzerland |
Document type | Journal Article |
ZDB-ID | 2662250-6 |
ISSN | 2075-1729 |
ISSN | 2075-1729 |
DOI | 10.3390/life12081277 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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