Topics:
machine learning
Complex Information for Anesthesiologists Presented Quickly and Clearly
Intraop, intraoperative; preop, preoperative.
Infographic created by Jonathan P. Wanderer, Vanderbilt University Medical Center, and James P. Rathmell, Brigham and Women’s Health Care/Harvard Medical School. Illustration by Annemarie Johnson, Vivo Visuals. Address correspondence to Dr. Wanderer: jonathan.p.wanderer@vanderbilt.edu.
1.
Mathis
MR
, Kheterpal
S
, Najarian
K
: Artificial intelligence for anesthesia: What the practicing clinician needs to know: More than black magic for the art of the dark.
Anesthesiology
2018
; 129
:619
–22
2.
Hatib
F
, Jian
Z
, Buddi
S
, Lee
C
, Settels
J
, Sibert
K
, Rinehart
J
, Cannesson
M
: Machine-learning algorithm to predict hypotension based on high-fidelity arterial pressure waveform analysis.
Anesthesiology
2018
; 129
:663
–74
3.
Kendale
S
, Kulkarni
P
, Rosenberg
AD
, Wang
J
: Supervised machine learning predictive analytics for prediction of postinduction hypotension.
Anesthesiology
2018
; 129
:675
–88
4.
Lee
CK
, Hofer
I
, Gabel
E
, Baldi
P
, Cannesson
M
: Development and validation of a deep neural network model for prediction of postoperative in-hospital mortality.
Anesthesiology
2018
; 129
:649
–62
Copyright © 2018, the American Society of Anesthesiologists, Inc. Wolters Kluwer Health, Inc. All Rights Reserved.
2018