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The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care
File | Description | Size | Format | |
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![]() | Accepted version | 406.04 kB | Microsoft Word | View/Open |
![]() | Supporting information | 62.18 kB | Microsoft Word | View/Open |
![]() | Supporting information | 546.72 kB | Microsoft Word | View/Open |
Title: | The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care |
Authors: | Komorowski, M Celi, LA Badawi, O Gordon, AC Faisal, A |
Item Type: | Journal Article |
Abstract: | Sepsis is the third leading cause of death worldwide and the main cause of mortality in hospitals1–3, but the best treatment strategy remains uncertain. In particular, evidence suggests that current practices in the administration of intravenous fluids and vasopressors are suboptimal and likely induce harm in a proportion of patients1,4–6. To tackle this sequential decision-making problem, we developed a reinforcement learning agent, the artificial intelligence (AI) Clinician, which learns from data to predict patient dynamics given specific treatment decisions. Our agent extracted implicit knowledge from an amount of patient data that exceeds many-fold the life-time experience of human clinicians and learned optimal treatment by having analysed myriads of (mostly sub-optimal) treatment decisions. We demonstrate that the value of the AI Clinician’s selected treatment is on average reliably higher than the human clinicians. In a large validation cohort independent from the training data, mortality was lowest in patients where clinicians’ actual doses matched the AI policy. Our model provides individualized and clinically interpretable treatment decisions for sepsis that could improve patient outcomes. |
Issue Date: | 22-Oct-2018 |
Date of Acceptance: | 13-Aug-2018 |
URI: | http://hdl.handle.net/10044/1/61246 |
DOI: | 10.1038/s41591-018-0213-5 |
ISSN: | 1078-8956 |
Publisher: | Nature Publishing Group |
Start Page: | 1716 |
End Page: | 1720 |
Journal / Book Title: | Nature Medicine |
Volume: | 24 |
Copyright Statement: | © 2018, Springer Nature Publishing AG |
Sponsor/Funder: | Engineering and Physical Sciences Research Council (EPSRC) & alumni |
Keywords: | Science & Technology Life Sciences & Biomedicine Biochemistry & Molecular Biology Cell Biology Medicine, Research & Experimental Research & Experimental Medicine INTERNATIONAL CONSENSUS DEFINITIONS SEPTIC SHOCK MORTALITY MEDICINE CRITERIA THERAPY ADULTS Administration, Intravenous Artificial Intelligence Clinical Decision-Making Cohort Studies Critical Care Female Humans Learning Male Sepsis Software Vasoconstrictor Agents Humans Sepsis Vasoconstrictor Agents Critical Care Cohort Studies Learning Artificial Intelligence Software Female Male Administration, Intravenous Clinical Decision-Making Immunology 11 Medical and Health Sciences |
Publication Status: | Published |
Online Publication Date: | 2018-10-22 |
Appears in Collections: | Bioengineering Department of Surgery and Cancer Computing Faculty of Medicine Faculty of Engineering |