Does the "Artificial Intelligence Clinician" learn optimal treatment strategies for sepsis in intensive care?

TL;DR

This paper examines the AI Clinician's ability to learn and suggest optimal treatment strategies for sepsis in intensive care, analyzing its decision-making process and potential clinical implications.

Contribution

It introduces methods to train an AI system for sepsis treatment and provides an analysis of its individual treatment recommendations.

Findings

AI Clinician learns treatment strategies for sepsis

Analysis of individual treatment profiles

Potential to improve clinical decision-making

Abstract

From 2017 to 2018 the number of scientific publications found via PubMed search using the keyword "Machine Learning" increased by 46% (4,317 to 6,307). The results of studies involving machine learning, artificial intelligence (AI), and big data have captured the attention of healthcare practitioners, healthcare managers, and the public at a time when Western medicine grapples with unmitigated cost increases and public demands for accountability. The complexity involved in healthcare applications of machine learning and the size of the associated data sets has afforded many researchers an uncontested opportunity to satisfy these demands with relatively little oversight. In a recent Nature Medicine article, "The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care," Komorowski and his coauthors propose methods to train an artificial intelligence clinician to treat sepsis patients with vasopressors and IV fluids. In this post, we will closely examine the claims laid out in this paper. In particular, we will study the individual treatment profiles suggested by their AI Clinician to gain insight into how their AI Clinician intends to treat patients on an individual level.