Understanding the artificial intelligence clinician and optimal
treatment strategies for sepsis in intensive care
treatment strategies for sepsis in intensive care
File(s)1903.02345v1.pdf (603.81 KB)
Working paper
Author(s)
Komorowski, Matthieu
Celi, Leo A
Badawi, Omar
Gordon, Anthony C
Faisal, A Aldo
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.
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.
Date Issued
2019-03-06
Citation
2019
Identifier
http://arxiv.org/abs/1903.02345v1
Subjects
cs.AI
cs.AI
stat.AP
Notes
13 pages and a number of figures