Timely prediction of clinically critical events in Intensive Care Unit (ICU) is important for improving care and survival rate. Most of the existing approaches are based on the application of various classification methods on explicitly extracted statistical features from vital signals. In this work, we propose to eliminate the high cost of engineering hand-crafted features from multivariate time-series of physiologic signals by learning their representation with a sequence-to-sequence auto-encoder. We then propose to hash the learned representations to enable signal similarity assessment for the prediction of critical events. We apply this methodological framework to predict Acute Hypotensive Episodes (AHE) on a large and diverse dataset of vital signal recordings. Experiments demonstrate the ability of the presented framework in accurately predicting an upcoming AHE.

Drones are a controversial tech gadget to say the least. They can pose a risk to aircraft, cause potential privacy issues, and are being used to smuggle contraband into prisons. Despite their bad reputation, a lot of research is being put into the use of unmanned aerial vehicles (UAVs) within emergency missions. At New York University's Abu Dhabi campus, Professor of Electrical and Computer Engineering, Antonios Tzes, has been manning a project across five different universities in the US, Sweden, Switzerland, Netherlands, and Greece, to develop drones for use inside buildings, particularly in fire situations. After designing ground vehicles for rescue operations, Tzes and his team were looking for a way to move away from the ground. "We needed to go up into the air, in confined spaces, and drones were the logical way to do it," he tells the Standard.