56 citations · 140 across the 10 of their papers we have counts for
10 papers
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…
Explainable artificial intelligence model to predict acute critical illness from electronic health records
Simon Meyer Lauritsen, Mads Kristensen, Mathias Vassard Olsen +5
We developed an explainable artificial intelligence (AI) early warning score (xAI-EWS) system for early detection of acute critical illness. While maintaining a high predictive per…
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…
Utilizing Device-level Demand Forecasting for Flexibility Markets - Full Version
Bijay Neupane, Torben Bach Pedersen, Bo Thiesson
The uncertainty in the power supply due to fluctuating Renewable Energy Sources (RES) has severe (financial and other) implications for energy market players. In this paper, we pre…
The Bregman Variational Dual-Tree Framework
Saeed Amizadeh, Bo Thiesson, Milos Hauskrecht
Graph-based methods provide a powerful tool set for many non-parametric frameworks in Machine Learning. In general, the memory and computational complexity of these methods is quad…
Score and Information for Recursive Exponential Models with Incomplete Data
Bo Thiesson
Recursive graphical models usually underlie the statistical modelling concerning probabilistic expert systems based on Bayesian networks. This paper defines a version of these mode…