Tag Archives: EHRs

Rethinking Streaming: Correct State Matters!

by Nesime Tatbul, Intel Labs and MIT CSAIL; Kristin Tufte, Portland State University; and Stan Zdonik, Brown University Stream processing has largely been thought of as real-time analytics. Data enters the system as streams and analytic functions (aggregates) are computed on the … Continue reading

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Medical Data and the Learning Healthcare System

By Tristan Naumann, PhD Candidate, MIT At the recent Intel Science and Technology Center for Big Data annual Research Retreat, Professor Peter Szolovits of MIT provided steps toward realizing the “learning healthcare system” described by the Institute of Medicine (IOM) of the … Continue reading

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Using Big Data to Predict Mortality in ICU Patients

Today, Monday, August 25 at the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining in New York, ISTC for Big Data researchers will present a paper entitled “Unfolding Physiological State: Mortality Modelling in Intensive Care Units.”  The paper … Continue reading

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Unfolding Physiological State and the Big Data Variety Challenge

By Tristan Naumann, MIT CSAIL* In exploring better ways to handle the challenge of Big Data Variety, the ISTC for Big Data has been working with the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) II database, which is composed of de-identified … Continue reading

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