English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Clinical trials involving multiple treatments utilize randomization of thetreatment assignments to enable the evaluation of treatment efficacies in anunbiased manner. Such evaluation is performed in post hoc studies that usuallyuse supervised-learning methods that rely on large amounts of data collected ina randomized fashion. That approach often proves to be suboptimal in that someparticipants may suffer and even die as a result of having not received themost appropriate treatments during the trial. Reinforcement-learning methodsimprove the situation by making it possible to learn the treatment efficaciesdynamically during the course of the trial, and to adapt treatment assignmentsaccordingly. Recent efforts using \textit{multi-arm bandits}, a type ofreinforcement-learning methods, have focused on maximizing clinical outcomesfor a population that was assumed to be homogeneous. However, those approacheshave failed to account for the variability among participants that is becomingincreasingly evident as a result of recent clinical-trial-based studies. Wepresent a contextual-bandit-based online treatment optimization algorithm that,in choosing treatments for new participants in the study, takes into accountnot only the maximization of the clinical outcomes but also the patientcharacteristics. We evaluated our algorithm using a real clinical trial datasetfrom the International Stroke Trial. The results of our retrospective analysisindicate that the proposed approach performs significantly better than either arandom assignment of treatments (the current gold standard) or amulti-arm-bandit-based approach, providing substantial gains in the percentageof participants who are assigned the most suitable treatments. Thecontextual-bandit and multi-arm bandit approaches provide 72.63% and 64.34%gains, respectively, compared to a random assignment.

A Contextual-bandit-based Approach for Informed Decision-making in Clinical Trials