MAN VS. MACHINE: COMPARISON OF A MACHINE LEARNING ALGORITHM TO CLINICIAN INTUITION FOR PREDICTING INTENSIVE CARE UNIT READMISSION

被引：0
|
作者：
Rojas, Juan C. [1 ]
Lyons, Patrick G. [2 ]
Kilaru, Megha [1 ]
Carey, Kyle A. [1 ]
Venable, Laura R. [1 ]
Picart, Jamila [1 ]
Mccauley, Leslie [1 ]
Arora, Vineet [1 ]
Edelson, Dana P. [1 ]
Churpek, Matthew M. [1 ]
机构：
[1] Univ Chicago, Chicago, IL 60637 USA
[2] Washington Univ, St Louis, MO 63110 USA
来源：
JOURNAL OF INVESTIGATIVE MEDICINE | 2019年 / 67卷 / 05期
关键词：
D O I：
10.1136/jim-2019-midwestern2019.103
中图分类号：
R5 [内科学];
学科分类号：
1002 ; 100201 ;
摘要：
A09
引用
收藏
页码：929 / 929
页数：1
相关论文
共 50 条
  • [31] Man vs. machine: Predicting hospital bed demand from an emergency department
    Lucini, Filipe Rissieri
    dos Reis, Mateus Augusto
    Caetano daSilveira, Giovani Jose
    Fogliatto, Flavio Sanson
    Anzanello, Michel Jose
    Andrioli, Giordanna Guerra
    Nicolaidis, Rafael
    Ferreira Beltrame, Rafael Coimbra
    Neyeloff, Jeruza Lavanholi
    Schaan, Beatriz D'Agord
    PLOS ONE, 2020, 15 (08):
  • [32] Pediatric Intensive Care Unit Length of Stay Prediction by Machine Learning
    Ganatra, Hammad A.
    Latifi, Samir Q.
    Baloglu, Orkun
    BIOENGINEERING-BASEL, 2024, 11 (10):
  • [33] Machine Learning for Acute Kidney Injury Prediction in the Intensive Care Unit
    Gottlieb, Eric R.
    Samuel, Mathew
    Bonventre, Joseph V.
    Celi, Leo A.
    Mattie, Heather
    ADVANCES IN CHRONIC KIDNEY DISEASE, 2022, 29 (05) : 431 - 438
  • [34] Machine Learning Techniques to Identify Antimicrobial Resistance in the Intensive Care Unit
    Martinez-Aguero, Sergio
    Mora-Jimenez, Inmaculada
    Lerida-Garcia, Jon
    Alvarez-Rodriguez, Joaquin
    Soguero-Ruiz, Cristina
    ENTROPY, 2019, 21 (06)
  • [35] Identifying and evaluating barriers for the implementation of machine learning in the intensive care unit
    D'Hondt, Ellie
    Ashby, Thomas J.
    Chakroun, Imen
    Koninckx, Thomas
    Wuyts, Roel
    COMMUNICATIONS MEDICINE, 2022, 2 (01):
  • [36] Identifying and evaluating barriers for the implementation of machine learning in the intensive care unit
    Ellie D’Hondt
    Thomas J. Ashby
    Imen Chakroun
    Thomas Koninckx
    Roel Wuyts
    Communications Medicine, 2
  • [37] Machine Learning to Support Hemodynamic Intervention in the Neonatal Intensive Care Unit
    Van Laere, David
    Meeus, Marisse
    Beirnaert, Charlie
    Sonck, Victor
    Laukens, Kris
    Mahieu, Ludo
    Mulder, Antonius
    CLINICS IN PERINATOLOGY, 2020, 47 (03) : 435 - +
  • [38] Machine learning for the prediction of delirium in elderly intensive care unit patients
    Ma, Rui
    Zhao, Jin
    Wen, Ziying
    Qin, Yunlong
    Yu, Zixian
    Yuan, Jinguo
    Zhang, Yumeng
    Wang, Anjing
    Li, Cui
    Li, Huan
    Chen, Yang
    Han, Fengxia
    Zhao, Yueru
    Sun, Shiren
    Ning, Xiaoxuan
    EUROPEAN GERIATRIC MEDICINE, 2024, : 1393 - 1403
  • [39] Prediction of early unplanned intensive care unit readmission in a UK tertiary care hospital: a cross-sectional machine learning approach
    Desautels, Thomas
    Das, Ritankar
    Calvert, Jacob
    Trivedi, Monica
    Summers, Charlotte
    Wales, David J.
    Ercole, Ari
    BMJ OPEN, 2017, 7 (09):
  • [40] A Machine Learning-Based Algorithm for the Prediction of Intensive Care Unit Delirium (PRIDE): Retrospective Study
    Hur, Sujeong
    Ko, Ryoung-Eun
    Yoo, Junsang
    Ha, Juhyung
    Cha, Won Chul
    Chung, Chi Ryang
    JMIR MEDICAL INFORMATICS, 2021, 9 (07)
12345