Cargando…

Machine learning for prediction of delirium in patients with extensive burns after surgery

AIMS: Machine learning‐based identification of key variables and prediction of postoperative delirium in patients with extensive burns. METHODS: Five hundred and eighteen patients with extensive burns who underwent surgery were included and randomly divided into a training set, a validation set, and...

Descripción completa

Detalles Bibliográficos
Autores principales:	Ren, Yujie, Zhang, Yu, Zhan, Jianhua, Sun, Junfeng, Luo, Jinhua, Liao, Wenqiang, Cheng, Xing
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	John Wiley and Sons Inc. 2023
Materias:	Original Articles
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10493655/ https://www.ncbi.nlm.nih.gov/pubmed/37122154 http://dx.doi.org/10.1111/cns.14237

Ejemplares similares

Research progress on risk factors of delirium in burn patients: A narrative review
por: Ren, Yujie, et al.
Publicado: (2022)

Machine learning-based prediction model for postoperative delirium in non-cardiac surgery
por: Lee, Dong Yun, et al.
Publicado: (2023)

Prediction of Mortality after Burn Surgery in Critically Ill Burn Patients Using Machine Learning Models
por: Park, Ji Hyun, et al.
Publicado: (2022)

Machine learning approach for predicting inhalation injury in patients with burns
por: Yang, Shih-Yi, et al.
Publicado: (2023)

Automated machine learning‐based model for the prediction of delirium in patients after surgery for degenerative spinal disease
por: Zhang, Yu, et al.
Publicado: (2022)

Development of postoperative delirium prediction models in patients undergoing cardiovascular surgery using machine learning algorithms
por: Nagata, Chie, et al.
Publicado: (2023)

Exploiting Machine Learning Algorithms and Methods for the Prediction of Agitated Delirium After Cardiac Surgery: Models Development and Validation Study
por: Mufti, Hani Nabeel, et al.
Publicado: (2019)

The effect of well-known burn-related features on machine learning algorithms in burn patients’ mortality prediction
por: Yazıcı, Hilmi, et al.
Publicado: (2023)

Delirium misdiagnosis risk in psychiatry: a machine learning-logistic regression predictive algorithm
por: Hercus, Catherine, et al.
Publicado: (2020)

Navigating the machine learning pipeline: a scoping review of inpatient delirium prediction models
por: Strating, Tom, et al.
Publicado: (2023)

Extensive Burns and Scalds
por: Wilson, W. C.
Publicado: (1935)

68 Using the Cornell Assessment of Pediatric Delirium Score to Identify Delirium in Pediatric Burn Patients
por: Bergus, Katherine, et al.
Publicado: (2023)

Using the Chinese version of Memorial Delirium Assessment Scale to describe postoperative delirium after hip surgery
por: Shi, Zhongyong, et al.
Publicado: (2014)

Postoperative delirium prediction using machine learning models and preoperative electronic health record data
por: Bishara, Andrew, et al.
Publicado: (2022)

Using Machine Learning Algorithms to Predict High-Risk Factors for Postoperative Delirium in Elderly Patients
por: Liu, Yuan, et al.
Publicado: (2023)

Improvement of Predictive Scores in Burn Medicine through Different Machine Learning Approaches
por: Schmidt, Sonja Verena, et al.
Publicado: (2023)

Comparison of logistic regression and machine learning methods for predicting postoperative delirium in elderly patients: A retrospective study
por: Song, Yu‐xiang, et al.
Publicado: (2022)

Dataset on acute hyperglycemia in extensively burned patients and mice
por: Li, Jun, et al.
Publicado: (2018)

Automated machine learning‐based model predicts postoperative delirium using readily extractable perioperative collected electronic data
por: Hu, Xiao‐Yi, et al.
Publicado: (2021)

Developing Machine Learning Algorithms to Predict Pulmonary Complications After Emergency Gastrointestinal Surgery
por: Xue, Qiong, et al.
Publicado: (2021)

Feature Extraction Based Machine Learning for Human Burn Diagnosis From Burn Images
Publicado: (2019)

A Novel Radiomics-Based Machine Learning Framework for Prediction of Acute Kidney Injury-Related Delirium in Patients Who Underwent Cardiovascular Surgery
por: Xue, Xin, et al.
Publicado: (2022)

A Machine Learning–Based Algorithm for the Prediction of Intensive Care Unit Delirium (PRIDE): Retrospective Study
por: Hur, Sujeong, et al.
Publicado: (2021)

Machine-learning model predicting postoperative delirium in older patients using intraoperative frontal electroencephalographic signatures
por: Röhr, Vera, et al.
Publicado: (2022)

Development of a machine learning-based prediction model for sepsis-associated delirium in the intensive care unit
por: Zhang, Yang, et al.
Publicado: (2023)

A Machine Learning Approach for Investigating Delirium as a Multifactorial Syndrome
por: Ocagli, Honoria, et al.
Publicado: (2021)

Prediction of Postoperative Delirium in Geriatric Hip Fracture Patients: A Clinical Prediction Model Using Machine Learning Algorithms
por: Oosterhoff, Jacobien H. F., et al.
Publicado: (2021)

A bibliometric analysis of publications on burn sepsis using VOSviewer
por: Cao, Zhi, et al.
Publicado: (2022)

Explainable machine-learning predictions for complications after pediatric congenital heart surgery
por: Zeng, Xian, et al.
Publicado: (2021)

Reconstructive Surgery of Extensive Face and Neck Burn Scars Using Tissue Expanders
por: Ashab Yamin, Mohammad Reza, et al.
Publicado: (2015)

Changes Produced in the Blood by Extensive Burns
por: Feltz,
Publicado: (1870)

Mr. Bradley's Case of Extensive Burn
por: Bradley, J. A.
Publicado: (1813)

Surgical options in extensive burns management
por: Niţescu, C, et al.
Publicado: (2012)

A machine-learning model to predict postoperative delirium following knee arthroplasty using electronic health records
por: Jung, Jong Wook, et al.
Publicado: (2022)

Predicting, preventing, and identifying delirium after cardiac surgery
por: O’Neal, Jason B., et al.
Publicado: (2016)

Development and Validation of a Delirium Risk Prediction Model for Elderly Patients Undergoing Elective Orthopedic Surgery
por: Guo, Yaxin, et al.
Publicado: (2023)

Novel application of an automated-machine learning development tool for predicting burn sepsis: proof of concept
por: Tran, Nam K., et al.
Publicado: (2020)

A Bayesian finite-element trained machine learning approach for predicting post-burn contraction
por: Egberts, Ginger, et al.
Publicado: (2022)

A machine learning approach to identifying delirium from electronic health records
por: Kim, Jae Hyun, et al.
Publicado: (2022)

Delirium detection using wearable sensors and machine learning in patients with intracerebral hemorrhage
por: Ahmed, Abdullah, et al.
Publicado: (2023)

Cannot write session to /tmp/vufind_sessions/sess_v0a2nu342p5vlskth2bj1k6prm