Real-time analysis for short-term prognosis in intensive care

There is a tremendous amount of data available to physicians at the point of care in intensive care environments; however, physicians do not have the tools to extract relevant clinical information in a timely manner. They mostly rely on manual inspection of the data to make diagnosis and prognosis. New software technologies make it possible to automatically generate meaningful information in real-time from the physiological data streams of patients. These real-time monitoring software technologies can support multiple concurrent patients and have been developed mainly to be applied in a reactive way, for the detection of patient complications. This paper proposes ways to extend these real-time monitoring technologies to help intensive care become more proactive. We present a system design and algorithms for a prototype system that produces in real-time short-term predictions of patient physiological data from live and historical patient data. One technique is based solely on the patient's own live data streams. The other technique is based on comparing the patient's physiological data streams with data streams of similar patients that have been monitored in the past. An extensive experimental study of this system is proposed to evaluate its predictive ability.