Big Data for Predictive Analytics in High Acuity Health Settings

  • John ZaleskiEmail author
Part of the Studies in Big Data book series (SBD, volume 42)


Automated data capture is more prevalent than ever in healthcare today. Electronic health record systems (EHRs) and real-time data from medical devices and laboratory equipment, imaging, and patient demographics have greatly increased the capability to closely monitor, diagnose, and administer therapies to patients. This chapter focuses on the use of data for in-patient care management in high-acuity spaces, such as operating rooms (ORs), intensive care units (ICUs) and emergency departments (EDs). In addition, a discussion of various types of mathematical techniques and approaches for identifying patients at risk will be discussed as well as the identification and challenges associated with issuing of alarm signals on monitored patients.


Real-time data Alarm signal Medical device data Adverse event Monitoring Wavelet transforms Kalman Filter Vital signs Early warning Periodograms 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Bernoulli, Enterprise, Inc.MilfordUSA

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