Data Driven Analytics for Personalized Healthcare

Part of the Health Informatics book series (HI)

Abstract

The concept of Learning Health Systems (LHS) is gaining momentum as more and more electronic healthcare data becomes increasingly accessible. The core idea is to enable learning from the collective experience of a care delivery network as recorded in the observational data, to iteratively improve care quality as care is being provided in a real world setting. In line with this vision, much recent research effort has been devoted to exploring machine learning, data mining and data visualization methodologies that can be used to derive real world evidence from diverse sources of healthcare data to provide personalized decision support for care delivery and care management. In this chapter, we will give an overview of a wide range of analytics and visualization components we have developed, examples of clinical insights reached from these components, and some new directions we are taking.

Keywords

Data driven healthcare analytics Learning health system Practice based evidence Real world evidence Clinical decision support Machine learning Data mining Data visualization 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Healthcare Analytics Research GroupIBM T.J. Watson Research CenterYorktown HeightsUSA
  2. 2.Computer Science and EngineeringUniversity of ConnecticutStorrsUSA

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