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Population-aware Hierarchical Bayesian Domain Adaptation

Vishwali Mhasawade,N. Rehman,R. Chunara

Published 2018 in arXiv.org

ABSTRACT

Population attributes are essential in health for understanding who the data represents and precision medicine efforts. Even within disease infection labels, patients can exhibit significant variability; "fever" may mean something different when reported in a doctor's office versus from an online app, precluding directly learning across different datasets for the same prediction task. This problem falls into the domain adaptation paradigm. However, research in this area has to-date not considered who generates the data; symptoms reported by a woman versus a man, for example, could also have different implications. We propose a novel population-aware domain adaptation approach by formulating the domain adaptation task as a multi-source hierarchical Bayesian framework. The model improves prediction in the case of largely unlabelled target data by harnessing both domain and population invariant information.

PUBLICATION RECORD

  • Publication year

    2018

  • Venue

    arXiv.org

  • Publication date

    2018-11-21

  • Fields of study

    Medicine, Computer Science, Mathematics

  • Identifiers
    arXiv 1811.08579
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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