Abstract: Big data has brought much promise for discovery of treatment and therapies, drug safety, and the care delivery processes by identifying which treatment would work best for which patients. NIH has recently taken the initiative, ‘All of Us’, to collect one million or more patients’ data (electronic health records (EHR), imaging, genomics, environmental data, etc.) over the next few years. The intensive care unit represents a unique data source in this context, with carefully captured detailed high volume data from different systems such as EHR, electrocardiogram, blood pressure, infusion pumps, and photoplethysmogram, among other data. However, the mere availability of data does not translate into knowledge or improved outcome. Questions remain on what data is needed, how to integrate these high volume data with high throughput infrastructure for near real-time decision making by the clinicians. In this panel, we present our work in integrating this heterogeneous high volume data with state of the art technologies for retrospective analysis and near real-time decision making with different systems such as Medical Information Mart from Intensive Care Unit (MIMIC III) and Artemis: both from technological and clinical perspectives.

Learning Objective 1: Identify the technological challenges to integrate and analyze high volume data in ICU setting, both for real time and retrospective analysis.

Learning Objective 2: Identify the need for such systems in care delivery process.

Learning Objective 3: Learn about specific clinical applications of such systems, in the domain of prediction of sepsis, multivariate alarm generation, and prediction of pneumonia.


Mohammad Adibuzzaman (Presenter)
Purdue University

Andrew James (Presenter)
The Hospital for Sick Children

John Zaleski (Presenter)

Peter Haug (Presenter)
University of Utah

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