In the long term, we hoped to design monitoring systems that not only synthesized and reported all relevant measurements to clinicians, but also formed pathophysiologic hypotheses that best explained the observed data. In early 2000, our group at the Laboratory of Computational Physiology at MIT recognized that the richness and detail of the collected data opened the feasibility of creating a new generation of monitoring systems to track the physiologic state of the patient, employing the power of modern signal processing, pattern recognition, computational modeling, and knowledge-based clinical reasoning. Staggering amounts of data are collected daily on each patient in an ICU: multi-channel waveform data sampled hundreds of times each second, vital sign time series updated each second or minute, alarms and alerts, lab results, imaging results, records of medication and fluid administration, staff notes and more. They are connected to an array of equipment and monitors, and are carefully attended by the clinical staff. Patients in hospital intensive care units (ICUs) are physiologically fragile and unstable, generally have life-threatening conditions, and require close monitoring and rapid therapeutic interventions.
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