The future of artificial intelligence in intensive care
moving from predictive to actionable AI
Jim M. Smit (Erasmus MC, TU Delft - Pattern Recognition and Bioinformatics)
Jesse Krijthe (TU Delft - Pattern Recognition and Bioinformatics)
Jasper van Bommel (Erasmus MC)
Diederik A.M.P.J. Gommers (TU Delft - Biomechanical Engineering)
Marcel JT Reinders (TU Delft - Pattern Recognition and Bioinformatics)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Artificial intelligence (AI) research in the intensive care unit (ICU) mainly focuses on developing models (from linear regression to deep learning) to predict out-
comes, such as mortality or sepsis [1, 2]. However, there is another important aspect of AI that is typically not framed as AI (although it may be more worthy of the name), which is the prediction of patient outcomes or events that would result from different actions, known as causal inference [3, 4]. This aspect of AI is crucial for decision-making in the ICU. To emphasize the impor- tance of causal inference, we propose to refer to any data- driven model used for causal inference tasks as ‘action- able AI’, as opposed to ‘predictive AI’, and discuss how these models could provide meaningful decision support in the ICU.
help_outline