SUBIC: A Supervised Bi-Clustering Approach for Precision Medicine
arXiv:1709.09929 · doi:10.1109/ICMLA.2017.00-68
Abstract
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
References in corpus (5)
- Natural Scales in Geographical Patterns
- Coupled Two-Way Clustering Analysis of Gene Microarray Data
- The Iterative Signature Algorithm for the analysis of large scale gene expression data
- Observational Data-Driven Modeling and Optimization of Manufacturing Processes
- SAFS: A Deep Feature Selection Approach for Precision Medicine