23 citations · 81 across the 7 of their papers we have counts for
4 papers · 1 filter
The Consequences of the Framing of Machine Learning Risk Prediction Models: Evaluation of Sepsis in General Wards
Simon Meyer Lauritsen, Bo Thiesson, Marianne Johansson Jørgensen +4
Objectives: To evaluate the consequences of the framing of machine learning risk prediction models. We evaluate how framing affects model performance and model learning in four dif…
Early detection of sepsis utilizing deep learning on electronic health record event sequences
Simon Meyer Lauritsen, Mads Ellersgaard Kalør, Emil Lund Kongsgaard +4
The timeliness of detection of a sepsis event in progress is a crucial factor in the outcome for the patient. Machine learning models built from data in electronic health records c…
Staged Mixture Modelling and Boosting
Christopher Meek, Bo Thiesson, David Heckerman
In this paper, we introduce and evaluate a data-driven staged mixture modeling technique for building density, regression, and classification models. Our basic approach is to seque…
Variational Dual-Tree Framework for Large-Scale Transition Matrix Approximation
Saeed Amizadeh, Bo Thiesson, Milos Hauskrecht
In recent years, non-parametric methods utilizing random walks on graphs have been used to solve a wide range of machine learning problems, but in their simplest form they do not s…