Machine Learning

SUBIC: A Supervised Bi-Clustering Approach for Precision Medicine

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  • arXiv
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    SUBIC: A Supervised Bi-Clustering Approach for Precision Medicine

    Traditional medicine typically applies one-size-fits-all treatment for the entire patient population whereas precision medicine develops tailored treatment schemes for different patient subgroups. The fact that some factors may be more significant for a specific patient subgroup motivates clinicians and medical researchers to develop new approaches to subgroup detection and analysis, which is an effective strategy to personalize treatment. In this study, we propose a novel patient subgroup detection method, called Supervised Biclustring (SUBIC) using convex optimization and apply our approach to detect patient subgroups and prioritize risk factors for hypertension (HTN) in a vulnerable demographic subgroup (African-American). Our approach not only finds patient subgroups with guidance of a clinically relevant target variable but also identifies and prioritizes risk factors by pursuing sparsity of the input variables and encouraging similarity among the input variables and between the input and target variables

    SUBIC: A Supervised Bi-Clustering Approach for Precision Medicine
    by Milad Zafar Nezhad, Dongxiao Zhu, Najibesadat Sadati, Kai Yang, Phillip Levy
    https://arxiv.org/pdf/1709.09929v1.pdf

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