4.6 Article

Development and validation of a practical machine learning model to predict sepsis after liver transplantation

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Critical Care Medicine

Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021

Laura Evans et al.

INTENSIVE CARE MEDICINE (2021)

Article Anesthesiology

Thromboelastography does not reduce transfusion requirements in liver transplantation: A propensity score-matched study

Rita Gaspari et al.

Summary: This study aimed to compare the total blood product requirements in liver transplantation assisted by TEG or CCTs. The results showed that TEG-guided transfusion did not reduce the intraoperative blood product consumption in the overall LT population, but may have some advantages in the high-MELD patient subgroup, warranting further investigation.

JOURNAL OF CLINICAL ANESTHESIA (2021)

Article Physiology

Artificial Intelligence May Predict Early Sepsis After Liver Transplantation

Rishikesan Kamaleswaran et al.

Summary: An artificial intelligence method was developed to predict post-operative sepsis by analyzing physiological data streams, resulting in a model with high sensitivity, specificity, and positive predictive value. The model, trained on ranked features, showed promising results in predicting sepsis 12 hours before onset.

FRONTIERS IN PHYSIOLOGY (2021)

Article Respiratory System

Development and performance assessment of novel machine learning models to predict pneumonia after liver transplantation

Chaojin Chen et al.

Summary: Postoperative pneumonia in orthotopic liver transplantation (OLT) patients is associated with various risk factors and clinical outcomes. The XGBoost model utilizing 14 common variables showed the best performance in predicting postoperative pneumonia.

RESPIRATORY RESEARCH (2021)

Review Gastroenterology & Hepatology

Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review

Ashley Spann et al.

HEPATOLOGY (2020)

Letter Critical Care Medicine

Machine learning in intensive care medicine: ready for take-off?

Lucas M. Fleuren et al.

INTENSIVE CARE MEDICINE (2020)

Article Computer Science, Interdisciplinary Applications

Prediction of sepsis patients using machine learning approach: A meta-analysis

Md. Mohaimenul Islam et al.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2019)

Article Anesthesiology

Deep-learning model for predicting 30-day postoperative mortality

Bradley A. Fritz et al.

BRITISH JOURNAL OF ANAESTHESIA (2019)

Article Computer Science, Artificial Intelligence

A predictive model for acute allograft rejection of liver transplantation

Chien-Liang Liu et al.

EXPERT SYSTEMS WITH APPLICATIONS (2018)

Article Cell Biology

Risk Factors for Sepsis Based on Sepsis-3 Criteria after Orthotopic Liver Transplantation

Yanling Wang et al.

MEDIATORS OF INFLAMMATION (2018)

Article Medicine, General & Internal

Derivation and Validation of Machine Learning Approaches to Predict Acute Kidney Injury after Cardiac Surgery

Hyung-Chul Lee et al.

JOURNAL OF CLINICAL MEDICINE (2018)

Article Multidisciplinary Sciences

The MELD-Plus: A generalizable prediction risk score in cirrhosis

Uri Kartoun et al.

PLOS ONE (2017)

Article Critical Care Medicine

Assessment of Global Incidence and Mortality of Hospital-treated Sepsis

Carolin Fleischmann et al.

AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE (2016)

Article Medicine, General & Internal

The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3)

Mervyn Singer et al.

JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2016)

Review Endocrinology & Metabolism

Muscle wasting: a nutritional criterion to prioritize patients for liver transplantation

Aldo J. Montano-Loza

CURRENT OPINION IN CLINICAL NUTRITION AND METABOLIC CARE (2014)

Article Medical Informatics

Predicting disease risks from highly imbalanced data using random forest

Mohammed Khalilia et al.

BMC MEDICAL INFORMATICS AND DECISION MAKING (2011)

Article Surgery

MELD and other factors associated with survival after liver transplantation

KVN Menon et al.

AMERICAN JOURNAL OF TRANSPLANTATION (2004)

Article Gastroenterology & Hepatology

Model for End-Stage Liver Disease (MELD) and allocation of donor livers

R Wiesner et al.

GASTROENTEROLOGY (2003)

Article Gastroenterology & Hepatology

A model to predict survival in patients with end-stage liver disease

PS Kamath et al.

HEPATOLOGY (2001)