Utilizing Machine Learning to Predict Workplace Violence in Hospitals

Random forest machine learning models are a form of classification model, which attempts to sort data into one of two predefined categories. When trained on a set of data from a hospital, where each entry is listed as either conditions for workplace violence or not, a random forest model can begin to classify new data as it comes in. We developed a way to automatically poll hospital systems for the required data needed to make a prediction on the potential for workplace violence at any one given moment. Our team was unable to gain access to real hospital data, so we researched risk levels associated with various factors, and then generated plausible sample data based on our research. Our model performed very well against our sample data, but would likely need to be retrained with real data

Copper-catalyzed addition of nucleophilic silicon to aldehydes

How to train your silane: A new family of chiral copper(I) complexes that bear a bifluoride counteranion were prepared and used in the first example of the enantioselective transfer of a silyl group to an aldehyde. This procedure provides fast access to non-racemic α-hydroxysilanes in high enantioselectivities. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim