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Machine Learning for Treatment Assignment: Improving Individualized Risk Attribution

Clinical studies model the average treatment effect (ATE), but apply this population-level effect to future individuals. Due to recent developments of machine learning algorithms with useful statistical guarantees, we argue instead for modeling the individualized treatment effect (ITE), which has be...

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Bibliografische gegevens
Gepubliceerd in:AMIA Annu Symp Proc
Hoofdauteurs: Weiss, Jeremy, Kuusisto, Finn, Boyd, Kendrick, Liu, Jie, Page, David
Formaat: Artigo
Taal:Inglês
Gepubliceerd in: American Medical Informatics Association 2015
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Online toegang:https://ncbi.nlm.nih.gov/pmc/articles/PMC4765638/
https://ncbi.nlm.nih.gov/pubmed/26958271
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