Skip to Main Content
Umbrella Alt Text
header search
Search
Advanced Search
Skip Nav Destination

Predicting Pressure Injury in Critical Care Patients: A Machine-Learning Model

Am J Crit Care (2018) 27 (6): 461–468.
This article has been cited by the following articles in journals that are participating in CrossRef Cited-by Linking.
Identification of immune-related genes in diagnosing atherosclerosis with rheumatoid arthritis through bioinformatics analysis and machine learning
  • Fuze Liu
  • Yue Huang
  • Fuhui Liu
  • Hai Wang
Frontiers in Immunology (2023) 14
WGCNA combined with machine learning algorithms for analyzing key genes and immune cell infiltration in heart failure due to ischemic cardiomyopathy
  • XiangJin Kong
  • HouRong Sun
  • KaiMing Wei
  • LingWei Meng
  • Xin Lv
  • ChuanZhen Liu
  • FuShun Lin
  • XingHua Gu
Frontiers in Cardiovascular Medicine (2023) 10
Assessment of the accuracy of the CALCULATE scale for pressure injury in critically ill patients
  • Greice Kelli Coelho de Souza
  • Dagmar Elaine Kaiser
  • Paloma Pereira Morais
  • Márcio Manozzo Boniatti
Australian Critical Care (2023) 36 (2): 195.
An Integrated System of Multifaceted Machine Learning Models to Predict If and When Hospital-Acquired Pressure Injuries (Bedsores) Occur
  • Odai Y. Dweekat
  • Sarah S. Lam
  • Lindsay McGrath
International Journal of Environmental Research and Public Health (2023) 20 (1): 828.
The Clinical Application of Machine Learning Models for Risk Analysis of Ramp Lesions in Anterior Cruciate Ligament Injuries
  • Yong-Beom Park
  • Hyojoon Kim
  • Han-Jun Lee
  • Suk-Ho Baek
  • Il-Youp Kwak
  • Seong Hwan Kim
The American Journal of Sports Medicine (2023) 51 (1): 107.
Application of Artificial Intelligence Methodologies to Chronic Wound Care and Management: A Scoping Review
  • Mai Dabas
  • Dafna Schwartz
  • Dimitri Beeckman
  • Amit Gefen
Advances in Wound Care (2023) 12 (4): 205.
Diagnostic, clustering, and immune cell infiltration analysis of m6A regulators in patients with sepsis
  • Fenghui Li
  • Yuan Zhang
  • Zhiyun Peng
  • Yingjing Wang
  • Zhaoshang Zeng
  • Zhongxiang Tang
Scientific Reports (2023) 13 (1)
The paradox of obesity in pressure ulcers of critically ill patients
  • Fujin Chen
  • Xiaobo Wang
  • Yujie Pan
  • Bukao Ni
  • Jianhua Wu
International Wound Journal (2023)
Machine Learning Techniques, Applications, and Potential Future Opportunities in Pressure Injuries (Bedsores) Management: A Systematic Review
  • Odai Y. Dweekat
  • Sarah S. Lam
  • Lindsay McGrath
International Journal of Environmental Research and Public Health (2023) 20 (1): 796.
An Integrated System of Braden Scale and Random Forest Using Real-Time Diagnoses to Predict When Hospital-Acquired Pressure Injuries (Bedsores) Occur
  • Odai Y. Dweekat
  • Sarah S. Lam
  • Lindsay McGrath
International Journal of Environmental Research and Public Health (2023) 20 (6): 4911.
Molecular mechanism and diagnostic marker investigation of endoplasmic reticulum stress on periodontitis
  • Qianqian Sun
  • Enqiang Zhu
BMC Oral Health (2023) 23 (1)
A systematic review of predictive models for hospital‐acquired pressure injury using machine learning
  • You Zhou
  • Xiaoxi Yang
  • Shuli Ma
  • Yuan Yuan
  • Mingquan Yan
Nursing Open (2023) 10 (3): 1234.
Identification of diagnostic biomarkers for idiopathic pulmonary hypertension with metabolic syndrome by bioinformatics and machine learning
  • Wenzhang Lu
  • Jinbo Huang
  • Qin Shen
  • Fei Sun
  • Jun Li
Scientific Reports (2023) 13 (1)
Identification of Immune-Associated Genes in Diagnosing Aortic Valve Calcification With Metabolic Syndrome by Integrated Bioinformatics Analysis and Machine Learning
  • Yufei Zhou
  • Wenxiang Shi
  • Di Zhao
  • Shengjue Xiao
  • Kai Wang
  • Jing Wang
Frontiers in Immunology (2022) 13
Predictive validity of the Jackson–Cubbin scale for pressure ulcers in intensive care unit patients: A meta‐analysis
  • Xiaoli Chen
  • Dongmei Diao
  • Lei Ye
Nursing in Critical Care (2022)
The use of machine learning for investigating the role of plastic surgeons in anatomical injuries: A retrospective observational study
  • Nam Kyu Lim
  • Jong Hyun Park
Medicine (2022) 101 (40): e30943.
Heterogeneity hampers the identification of general pressure injury risk factors in intensive care populations: A predictive modelling analysis
  • Mieke Deschepper
  • Sonia O. Labeau
  • Willem Waegeman
  • Stijn I. Blot
Intensive and Critical Care Nursing (2022) 68: 103117.
Development and Validation of a Machine Learning-Based Nomogram for Prediction of Ankylosing Spondylitis
  • Jichong Zhu
  • Qing Lu
  • Tuo Liang
  • Hao Li
  • Chenxin Zhou
  • Shaofeng Wu
  • Tianyou Chen
  • Jiarui Chen
  • Guobing Deng
  • Yuanlin Yao
  • Shian Liao
  • Chaojie Yu
  • Shengsheng Huang
  • Xuhua Sun
  • Liyi Chen
  • Wenkang Chen
  • Zhen Ye
  • Hao Guo
  • Wuhua Chen
  • Wenyong Jiang
  • Binguang Fan
  • Xiang Tao
  • Xinli Zhan
  • Chong Liu
Rheumatology and Therapy (2022) 9 (5): 1377.
Explainable Artificial Intelligence for Predicting Hospital-Acquired Pressure Injuries in COVID-19–Positive Critical Care Patients
  • Jenny Alderden
  • Susan M. Kennerly
  • Andrew Wilson
  • Jonathan Dimas
  • Casey McFarland
  • David Y. Yap
  • Lucy Zhao
  • Tracey L. Yap
CIN: Computers, Informatics, Nursing (2022) 40 (10): 659.
Machine Learning Approaches for Hospital Acquired Pressure Injuries: A Retrospective Study of Electronic Medical Records
  • Joshua J. Levy
  • Jorge F. Lima
  • Megan W. Miller
  • Gary L. Freed
  • A. James O'Malley
  • Rebecca T. Emeny
Frontiers in Medical Technology (2022) 4
Machine Learning-Based Pressure Ulcer Prediction in Modular Critical Care Data
  • Petr Šín
  • Alica Hokynková
  • Nováková Marie
  • Pokorná Andrea
  • Rostislav Krč
  • Jan Podroužek
Diagnostics (2022) 12 (4): 850.
Pressure Injuries in Critical Care Patients in US Hospitals
  • Jill Cox
  • Laura E. Edsberg
  • Kimberly Koloms
  • Catherine A. VanGilder
Journal of Wound, Ostomy & Continence Nursing (2022) 49 (1): 21.
Integrative Analysis of Biomarkers and Mechanisms in Adamantinomatous Craniopharyngioma
  • Da Lin
  • Wenyue Zhao
  • Jun Yang
  • Hao Wang
  • Hongbing Zhang
Frontiers in Genetics (2022) 13
A Hybrid System of Braden Scale and Machine Learning to Predict Hospital-Acquired Pressure Injuries (Bedsores): A Retrospective Observational Cohort Study
  • Odai Y. Dweekat
  • Sarah S. Lam
  • Lindsay McGrath
Diagnostics (2022) 13 (1): 31.
Integrative Analyses of Genes Associated With Otologic Disorders in Turner Syndrome
  • Ruoyan Xue
  • Qi Tang
  • Yongli Zhang
  • Mengyao Xie
  • Chen Li
  • Shu Wang
  • Hua Yang
Frontiers in Genetics (2022) 13
The predictive effect of different machine learning algorithms for pressure injuries in hospitalized patients: A network meta-analyses
  • Chaoran Qu
  • Weixiang Luo
  • Zhixiong Zeng
  • Xiaoxu Lin
  • Xuemei Gong
  • Xiujuan Wang
  • Yu Zhang
  • Yun Li
Heliyon (2022) 8 (11): e11361.
Identification of diagnostic gene biomarkers and immune infiltration in patients with diabetic kidney disease using machine learning strategies and bioinformatic analysis
  • Shaojie Fu
  • Yanli Cheng
  • Xueyao Wang
  • Jingda Huang
  • Sensen Su
  • Hao Wu
  • Jinyu Yu
  • Zhonggao Xu
Frontiers in Medicine (2022) 9
Injuries in College Basketball Sports Based on Machine Learning from the Perspective of the Integration of Sports and Medicine
  • Dongming Zhao
  • Rahim Khan
Computational Intelligence and Neuroscience (2022) 2022: 1.
Identification and external validation of the hub genes associated with cardiorenal syndrome through time-series and network analyses
  • Jingjing Liang
  • Xiaohui Huang
  • Weiwen Li
  • Yunzhao Hu
Aging (2022) 14 (3): 1351.
Pressure Injury Risk Assessment and Prevention in Patients With COVID-19 in the Intensive Care Unit
  • Jenny Alderden
  • Susan M. Kennerly
  • Jill Cox
  • Tracey L. Yap
AACN Advanced Critical Care (2022) 33 (2): 173.
A machine learning algorithm for early detection of heel deep tissue injuries based on a daily history of sub‐epidermal moisture measurements
  • Maayan Lustig
  • Dafna Schwartz
  • Ruth Bryant
  • Amit Gefen
International Wound Journal (2022) 19 (6): 1339.
Exploring the key ferroptosis-related gene in the peripheral blood of patients with Alzheimer’s disease and its clinical significance
  • Xiaonan Wang
  • Yaotian Tian
  • Chunmei Li
  • Min Chen
Frontiers in Aging Neuroscience (2022) 14
Artificial intelligence in dermatology and healthcare: An overview
  • Varadraj Vasant Pai
  • Rohini Bhat Pai
Indian Journal of Dermatology, Venereology and Leprology (2021) 87: 457.
Exploring personal chemical exposures in China with wearable air pollutant monitors: A repeated-measure study in healthy older adults in Jinan, China
  • Pengfei Guo
  • Elizabeth Z. Lin
  • Jeremy P. Koelmel
  • Enmin Ding
  • Ying Gao
  • Fuchang Deng
  • Haoran Dong
  • Yuanyuan Liu
  • Yu'e Cha
  • Jianlong Fang
  • Xiaoming Shi
  • Song Tang
  • Krystal J. Godri Pollitt
Environment International (2021) 156: 106709.
Reducing the Incidence of Medical Device–Related Pressure Injuries From Use of CPAP/BiPAP Masks
  • Linda Arundel
  • Evelyn Irani
  • George Barkema
Journal of Wound, Ostomy & Continence Nursing (2021) 48 (2): 108.
Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review
  • Kathrin Seibert
  • Dominik Domhoff
  • Dominik Bruch
  • Matthias Schulte-Althoff
  • Daniel Fürstenau
  • Felix Biessmann
  • Karin Wolf-Ostermann
Journal of Medical Internet Research (2021) 23 (11): e26522.
Scientific and Clinical Abstracts From WOCNext® 2021
Journal of Wound, Ostomy & Continence Nursing (2021) 48 (3S): S1.
A 9 mRNAs-based diagnostic signature for rheumatoid arthritis by integrating bioinformatic analysis and machine-learning
  • Jianyong Liu
  • Ningjie Chen
Journal of Orthopaedic Surgery and Research (2021) 16 (1)
Hospital acquired pressure injury prediction in surgical critical care patients
  • Jenny Alderden
  • Kathryn P. Drake
  • Andrew Wilson
  • Jonathan Dimas
  • Mollie R. Cummins
  • Tracey L. Yap
BMC Medical Informatics and Decision Making (2021) 21 (1)
The potential of artificial intelligence to improve patient safety: a scoping review
  • David W. Bates
  • David Levine
  • Ania Syrowatka
  • Masha Kuznetsova
  • Kelly Jean Thomas Craig
  • Angela Rui
  • Gretchen Purcell Jackson
  • Kyu Rhee
npj Digital Medicine (2021) 4 (1)
Pressure injury prediction models for critically-ill patients should consider both the case-mix and local factors
  • Mieke Deschepper
  • Sonia O. Labeau
  • Willem Waegeman
  • Stijn I. Blot
Intensive and Critical Care Nursing (2021) 65: 103033.
Performance Evaluation of Multiple Ultrasonographical Methods for the Detection of Primary Sjögren’s Syndrome
  • Shihao Xu
  • Jing Luo
  • Chengwei Zhu
  • Jiachun Jiang
  • Hui Cheng
  • Ping Wang
  • Jingwei Hong
  • Jinxia Fang
  • Jingjing Pan
  • Matthew A. Brown
  • Xiaochun Zhu
  • Xiaobing Wang
Frontiers in Immunology (2021) 12
Predicting pressure injury using nursing assessment phenotypes and machine learning methods
  • Wenyu Song
  • Min-Jeoung Kang
  • Linying Zhang
  • Wonkyung Jung
  • Jiyoun Song
  • David W Bates
  • Patricia C Dykes
Journal of the American Medical Informatics Association (2021) 28 (4): 759.
Research on the prevention of pressure injuries in adult intensive care unit patients. Where are we today and where should we go?
  • Mireia Llaurado-Serra
  • Sonia Labeau
Intensive and Critical Care Nursing (2021) 62: 102959.
Dynamic Risk Prediction for Hospital-Acquired Pressure Injury in Adult Critical Care Patients
  • Amy M. Shui
  • Phillip Kim
  • Vamsi Aribindi
  • Chiung-Yu Huang
  • Mi-Ok Kim
  • Sachin Rangarajan
  • Kaelan Schorger
  • J. Matthew Aldrich
  • Hanmin Lee
Critical Care Explorations (2021) 3 (11): e0580.
Using Machine Learning Technologies in Pressure Injury Management: Systematic Review
  • Mengyao Jiang
  • Yuxia Ma
  • Siyi Guo
  • Liuqi Jin
  • Lin Lv
  • Lin Han
  • Ning An
JMIR Medical Informatics (2021) 9 (3): e25704.
The Random Forest Model Has the Best Accuracy Among the Four Pressure Ulcer Prediction Models Using Machine Learning Algorithms
  • Jie Song
  • Yuan Gao
  • Pengbin Yin
  • Yi Li
  • Yang Li
  • Jie Zhang
  • Qingqing Su
  • Xiaojie Fu
  • Hongying Pi
Risk Management and Healthcare Policy (2021) Volume 14: 1175.
Literature Review of Machine-Learning Algorithms for Pressure Ulcer Prevention: Challenges and Opportunities
  • Fernando Ribeiro
  • Filipe Fidalgo
  • Arlindo Silva
  • José Metrôlho
  • Osvaldo Santos
  • Rogério Dionisio
Informatics (2021) 8 (4): 76.
Supervised machine learning-based prediction for in-hospital pressure injury development using electronic health records: A retrospective observational cohort study in a university hospital in Japan
  • Gojiro Nakagami
  • Shinichiroh Yokota
  • Aya Kitamura
  • Toshiaki Takahashi
  • Kojiro Morita
  • Hiroshi Noguchi
  • Kazuhiko Ohe
  • Hiromi Sanada
International Journal of Nursing Studies (2021) 119: 103932.
CE: Nursing Orientation to Data Science and Machine Learning
  • Roxanne L. O'Brien
  • Matt W. O'Brien
AJN, American Journal of Nursing (2021) 121 (4): 32.
  • Online ISSN 1937-710X
  • Print ISSN 1062-3264

JOURNALS

American Association of Critical-Care Nurses
27071 Aliso Creek Road
Aliso Viejo, CA 92656
  • Tel: (800) 899-1712
  • Tel: (949) 362-2000
Close Modal
This Feature Is Available To Subscribers Only

or Create an Account

Close Modal
Close Modal
This site uses cookies. By continuing to use our website, you are agreeing to our privacy policy.
Accept