Big Data Analytics in Healthcare

Data science plays an important role in many industries. In facing massive amount of heterogeneous data, scalable machine learning and data mining algorithms and systems become extremely important for data scientists. The growth of volume, complexity and speed in data drives the need for scalable data analytic algorithms and systems. In this course, we study such algorithms and systems in the context of healthcare applications.

In healthcare, large amounts of heterogeneous medical data have become available in various healthcare organizations (payers, providers, pharmaceuticals). This data could be an enabling resource for deriving insights for improving care delivery and reducing waste. The enormity and complexity of these datasets present great challenges in analyses and subsequent applications to a practical clinical environment.

• Big Data
• Predictive Modeling,Dimensionality Reduction & Tensor Factorization,Graph Analysis
• Healthcare
• Computational Phenotyping,Patient Similarity Metrics,Medical Ontology
• Technologies
• MapReduce,Spark,Hadoop