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Patient monitors in hospitals generate a high number of false alarms that compromise patients care and burden clinicians. In our previous work, an attempt to alleviate this problem by finding combinations of monitor alarms and laboratory test that were predictive of code blue events, called SuperAlarms. Our current work consists of developing a novel time… (More)

Contribution Many frequently arising problems involve finding the small-dimensional subspace that best captures the variablity of a set of observations belonging to a larger space, for example, finding its principal components. We propose an algorithm that finds this subspace through a series of orthogonal rotations, each represented as the exponential of a… (More)

A procedure is developed for obtaining the lower dimensional representation of high-dimensional observations stemming from different classes that best distinguishes among the classes. The method finds a low dimensional subspace such that the estimated probability of the projected data belonging to each population is as close to the true assignment as… (More)

- Rebeca Salas-Boni, Tomoko Oyama, Marta Zlatic, Esteban Tabak
- 2013

Background: Traditional methods for deciding whether two populations are distinguishable or not often

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