Combining Kernel and Model Based Learning for HIV Therapy Selection

Parbhoo, Sonali and Bogojeska, Jasmina and Zazzi, Maurizio and Roth, Volker and Doshi-Velez, Finale. (2017) Combining Kernel and Model Based Learning for HIV Therapy Selection. Amia Summits on Translational Science Proceedings, 2017. pp. 239-248.

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Official URL: https://edoc.unibas.ch/59234/

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We present a mixture-of-experts approach for HIV therapy selection. The heterogeneity in patient data makes it difficult for one particular model to succeed at providing suitable therapy predictions for all patients. An appropriate means for addressing this heterogeneity is through combining kernel and model-based techniques. These methods capture different kinds of information: kernel-based methods are able to identify clusters of similar patients, and work well when modelling the viral response for these groups. In contrast, model-based methods capture the sequential process of decision making, and are able to find simpler, yet accurate patterns in response for patients outside these groups. We take advantage of this information by proposing a mixture-of-experts model that automatically selects between the methods in order to assign the most appropriate therapy choice to an individual. Overall, we verify that therapy combinations proposed using this approach significantly outperform previous methods.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Informatik > Biomedical Data Analysis (Roth)
UniBasel Contributors:Roth, Volker and Parbhoo, Sonali
Item Type:Article, refereed
Article Subtype:Research Article
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:09 Feb 2018 13:24
Deposited On:09 Feb 2018 13:24

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