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pro vyhledávání: '"Lash, Michael T."'
Autor:
Lash, Michael T.
The use of machine learning (ML) models in decision-making contexts, particularly those used in high-stakes decision-making, are fraught with issue and peril since a person - not a machine - must ultimately be held accountable for the consequences of
Externí odkaz:
http://arxiv.org/abs/2206.01343
Publikováno v:
In Decision Support Systems January 2024 176
Autor:
Lash, Michael T., Street, W. Nick
Cardiovascular disease (CVD) is a serious illness affecting millions world-wide and is the leading cause of death in the US. Recent years, however, have seen tremendous growth in the area of personalized medicine, a field of medicine that places the
Externí odkaz:
http://arxiv.org/abs/2011.08254
Sepsis is one of the leading causes of death in Intensive Care Units (ICU). The strategy for treating sepsis involves the infusion of intravenous (IV) fluids and administration of antibiotics. Determining the optimal quantity of IV fluids is a challe
Externí odkaz:
http://arxiv.org/abs/2009.07963
Publikováno v:
In Journal of Business Research March 2023 157
Neural networks are capable of learning rich, nonlinear feature representations shown to be beneficial in many predictive tasks. In this work, we use such models to explore different geographical feature representations in the context of predicting c
Externí odkaz:
http://arxiv.org/abs/1809.03323
Inverse classification uses an induced classifier as a queryable oracle to guide test instances towards a preferred posterior class label. The result produced from the process is a set of instance-specific feature perturbations, or recommendations, t
Externí odkaz:
http://arxiv.org/abs/1802.04918
This large-scale study, consisting of 21.3 million hand hygiene opportunities from 19 distinct facilities in 10 different states, uses linear predictive models to expose factors that may affect hand hygiene compliance. We examine the use of features
Externí odkaz:
http://arxiv.org/abs/1801.09546
Neural networks are capable of learning rich, nonlinear feature representations shown to be beneficial in many predictive tasks. In this work, we use these models to explore the use of geographical features in predicting colorectal cancer survival cu
Externí odkaz:
http://arxiv.org/abs/1708.04714