University of California

Yong Bai

Fenyong Liu

Sangwei Lu

Hao Gong

Xiao Hu

Rebeca Salas-Boni

Methods for predicting patient deterioration or clinical events by detecting patterns in heterogeneous temporal clinical data streams that are predictive of certain clinical end points and matching the patient state with those patterns are described. The detected patterns, referred to as SuperAlarm triggers, are a predictive combination of frequently co-occurring monitor alarms, conditions and laboratory test results that can predict patient deterioration for imminent life-threatening events. SuperAlarm triggers may also exhibit patterns in the sequence of SuperAlarms that are triggered over the monitoring time of a patient. Sequential patterns of SuperAlarm triggers may also indicate a temporal process of change in patient status. SuperAlarm based alerts will also greatly reduce the number of false alarms and alarm fatigue compared to conventional alarms.

Recognizing predictive patterns in the sequence of superalarm triggers for predicting patient deterioration

The present invention provides vectors, including a novel attenuated strain of, for efficient gene transfer into an animal, e.g. a mammalian host.

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the Present Invention Provides Vectors, Including a Novel Attenuated Strain of, for Efficient Gene Transfer into an Animal, e.g. a Mammalian Host.

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Albany, CA, United States of America

Yong Bai