Understanding the artificial intelligence clinician and optimal treatment strategies for sepsis in intensive care

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Title: Understanding the artificial intelligence clinician and optimal treatment strategies for sepsis in intensive care
Authors: Komorowski, M
Celi, LA
Badawi, O
Gordon, AC
Faisal, AA
Item Type: Working Paper
Abstract: In this document, we explore in more detail our published work (Komorowski, Celi, Badawi, Gordon, & Faisal, 2018) for the benefit of the AI in Healthcare research community. In the above paper, we developed the AI Clinician system, which demonstrated how reinforcement learning could be used to make useful recommendations towards optimal treatment decisions from intensive care data. Since publication a number of authors have reviewed our work (e.g. Abbasi, 2018; Bos, Azoulay, & Martin-Loeches, 2019; Saria, 2018). Given the difference of our framework to previous work, the fact that we are bridging two very different academic communities (intensive care and machine learning) and that our work has impact on a number of other areas with more traditional computer-based approaches (biosignal processing and control, biomedical engineering), we are providing here additional details on our recent publication.
Issue Date: 6-Mar-2019
URI: http://hdl.handle.net/10044/1/71475
Keywords: cs.AI
cs.AI
stat.AP
cs.AI
cs.AI
stat.AP
Notes: 13 pages and a number of figures
Appears in Collections:Bioengineering
Division of Surgery
Computing
Faculty of Medicine



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