Healthcare Data Mining with Matrix Models

author: Joel Dudley, Icahn School of Medicine at Mount Sinai
author: Ping Zhang, IBM Thomas J. Watson Research Center
author: Fei Wang, Department of Healthcare Policy and Research, Cornell University
published: Sept. 9, 2016,   recorded: August 2016,   views: 2374
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Description

In the last decade, advances in high-throughput technologies, growth of clinical data warehouses, and rapid accumulation of biomedical knowledge provided unprecedented opportunities and challenges to researchers in biomedical informatics. One distinct solution, to efficiently conduct big data analytics for biomedical problems, is the application of matrix computation and factorization methods such as non-negative matrix factorization, joint matrix factorization, tensor factorization. Compared to probabilistic and information theoretic approaches, matrix-based methods are fast, easy to understand and implement. In this tutorial, we provide a review of recent advances in algorithms and methods using matrix and their potential applications in biomedical informatics. We survey various related articles from data mining venues as well as from biomedical informatics venues to share with the audience key problems and trends in matrix computation research, with different novel applications such as drug repositioning, personalized medicine, and electronic phenotyping.

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