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

Decision makers, such as doctors and judges, make crucial decisions such asrecommending treatments to patients, and granting bails to defendants on adaily basis. Such decisions typically involve weighting the potential benefitsof taking an action against the costs involved. In this work, we aim toautomate this task of learning {cost-effective, interpretable and actionabletreatment regimes. We formulate this as a problem of learning a decision list-- a sequence of if-then-else rules -- which maps characteristics of subjects(eg., diagnostic test results of patients) to treatments. We propose a novelobjective to construct a decision list which maximizes outcomes for thepopulation, and minimizes overall costs. We model the problem of learning sucha list as a Markov Decision Process (MDP) and employ a variant of the UpperConfidence Bound for Trees (UCT) strategy which leverages customized checks forpruning the search space effectively. Experimental results on real worldobservational data capturing treatment recommendations for asthma patientsdemonstrate the effectiveness of our approach.

Learning Cost-Effective and Interpretable Regimes for Treatment Recommendation