no code implementations • 26 Jan 2021 • Simon Meyer Lauritsen, Bo Thiesson, Marianne Johansson Jørgensen, Anders Hammerich Riis, Ulrick Skipper Espelund, Jesper Bo Weile, Jeppe Lange
The importance of proper problem framing is by no means exclusive to sepsis prediction and applies to most clinical risk prediction models.
no code implementations • 3 Dec 2019 • Simon Meyer Lauritsen, Mads Kristensen, Mathias Vassard Olsen, Morten Skaarup Larsen, Katrine Meyer Lauritsen, Marianne Johansson Jørgensen, Jeppe Lange, Bo Thiesson
We developed an explainable artificial intelligence (AI) early warning score (xAI-EWS) system for early detection of acute critical illness.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+3
no code implementations • 7 Jun 2019 • Simon Meyer Lauritsen, Mads Ellersgaard Kalør, Emil Lund Kongsgaard, Katrine Meyer Lauritsen, Marianne Johansson Jørgensen, Jeppe Lange, Bo Thiesson
We present a deep learning system for early detection of sepsis that is able to learn characteristics of the key factors and interactions from the raw event sequence data itself, without relying on a labor-intensive feature extraction work.
no code implementations • 2 May 2018 • Bijay Neupane, Torben Bach Pedersen, Bo Thiesson
In a typical device-level flexibility forecast, a market player is more concerned with the \textit{utility} that the demand flexibility brings to the market, rather than the intrinsic forecast accuracy.
no code implementations • 26 Sep 2013 • Saeed Amizadeh, Bo Thiesson, Milos Hauskrecht
Graph-based methods provide a powerful tool set for many non-parametric frameworks in Machine Learning.
no code implementations • 30 Jan 2013 • Bo Thiesson, Christopher Meek, David Maxwell Chickering, David Heckerman
We describe computationally efficient methods for learning mixtures in which each component is a directed acyclic graphical model (mixtures of DAGs or MDAGs).
no code implementations • NeurIPS 2010 • Bo Thiesson, Chong Wang
Remarkably easy implementation and guaranteed convergence has made the EM algorithm one of the most used algorithms for mixture modeling.