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arXiv · 1812.00509

Knowledge-driven generative subspaces for modeling multi-view dependencies in medical data

Parvathy Sudhir Pillai·Tze-Yun Leong
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Abstract

Early detection of Alzheimer's disease (AD) and identification of potential risk/beneficial factors are important for planning and administering timely interventions or preventive measures. In this paper, we learn a disease model for AD that combines genotypic and phenotypic profiles, and cognitive health metrics of patients. We propose a probabilistic generative subspace that describes the correlative, complementary and domain-specific semantics of the dependencies in multi-view, multi-modality medical data. Guided by domain knowledge and using the latent consensus between abstractions of multi-view data, we model the fusion as a data generating process. We show that our approach can potentially lead to i) explainable clinical predictions and ii) improved AD diagnoses.

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Parvathy Sudhir Pillai, Tze-Yun Leong. 2018-12-03. Knowledge-driven generative subspaces for modeling multi-view dependencies in medical data. https://arxiv.org/abs/1812.00509

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