Methodology minute: a machine learning primer for infection prevention and control

被引:0
作者
Wiemken, Timothy L. [1 ,2 ,3 ,4 ]
Rutschman, Ana Santos [3 ,4 ,5 ]
机构
[1] St Louis Univ, Dept Hlth & Clin Outcomes Res, Sch Med, St Louis, MO 63104 USA
[2] St Louis Univ, Dept Med, Div Infect Dis Allergy & Immunol, Sch Med, St Louis, MO 63104 USA
[3] St Louis Univ, Adv Hlth Data AHeaD Res Inst, Ctr Syst Infect Prevent, St Louis, MO 63104 USA
[4] St Louis Univ, Hlth Innovat & Legal Preparedness Partnership, St Louis, MO 63104 USA
[5] St Louis Univ, Sch Law, Ctr Hlth Law Studies, St Louis, MO 63104 USA
关键词
Artificial intelligence; Deep learning; Healthcare-associated infection; Natural language processing; Supervised learning; Statistical learning;
D O I
10.1016/j.ajic.2020.09.009
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
The use of machine-learning and predictive modeling in infection prevention and control activities is increasing dramatically. In order for infection preventionists to make informed decisions on the performance of any particular model as well as to determine if the output of the model will be useful for their program needs, a suitable understanding of the creation and evaluation of these models is necessary. The purpose of this primer is to introduce the infection preventionist to the most commonly used machine-learning method in infection prevention: supervised learning. (C) 2020 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.
引用
收藏
页码:1504 / 1505
页数:2
相关论文
共 11 条
[1]  
[Anonymous], 2014, SMBM
[2]   A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models [J].
Christodoulou, Evangelia ;
Ma, Jie ;
Collins, Gary S. ;
Steyerberg, Ewout W. ;
Verbakel, Jan Y. ;
Van Calster, Ben .
JOURNAL OF CLINICAL EPIDEMIOLOGY, 2019, 110 :12-22
[3]   Detecting hospital-acquired infections: A document classification approach using support vector machines and gradient tree boosting [J].
Ehrentraut, Claudia ;
Ekholm, Markus ;
Tanushi, Hideyuki ;
Tiedemann, Jorg ;
Dalianis, Hercules .
HEALTH INFORMATICS JOURNAL, 2018, 24 (01) :24-42
[4]  
Ishwaran H, 2020, J THORAC CARDIOVASC, P1
[5]  
Jacobson O, 2016, P 15 WORKSH BIOM NAT
[6]   Computationally Optimized SARS-CoV-2 MHC Class I and II Vaccine Formulations Predicted to Target Human Haplotype Distributions [J].
Liu, Ge ;
Carter, Brandon ;
Bricken, Trenton ;
Jain, Siddhartha ;
Viard, Mathias ;
Carrington, Mary ;
Gifford, David K. .
CELL SYSTEMS, 2020, 11 (02) :131-+
[7]   A Generalizable, Data-Driven Approach to Predict Daily Risk of Clostridium difficile Infection at Two Large Academic Health Centers [J].
Oh, Jeeheh ;
Makar, Maggie ;
Fusco, Christopher ;
McCaffrey, Robert ;
Rao, Krishna ;
Ryan, Erin E. ;
Washer, Laraine ;
West, Lauren R. ;
Young, Vincent B. ;
Guttag, John ;
Hooper, David C. ;
Shenoy, Erica S. ;
Wiens, Jenna .
INFECTION CONTROL AND HOSPITAL EPIDEMIOLOGY, 2018, 39 (04) :425-433
[8]  
2019, [No title captured]
[9]   Detection of Healthcare-Associated Urinary Tract Infection in Swedish Electronic Health Records [J].
Tanushi, Hideyuki ;
Kvist, Maria ;
Sparrelid, Elda .
INNOVATION IN MEDICINE AND HEALTHCARE 2014, 2014, 207 :330-339
[10]   High-performance medicine: the convergence of human and artificial intelligence [J].
Topol, Eric J. .
NATURE MEDICINE, 2019, 25 (01) :44-56