4.5 Article

Comparing machine learning and logistic regression for acute kidney injury prediction in trauma patients: A retrospective observational study at a single tertiary medical center

Related references

Note: Only part of the references are listed.
Article Medical Informatics

Machine learning model for predicting acute kidney injury progression in critically ill patients

Canzheng Wei et al.

Summary: This study aimed to develop a prediction model for determining the progression from AKI stage 1/2 to AKI stage 3. Logistic regression and machine learning extreme gradient boosting (XGBoost) were used to build the models, with the XGBoost model outperforming the logistic regression model in predicting AKI stage 3 progression.

BMC MEDICAL INFORMATICS AND DECISION MAKING (2022)

Review Computer Science, Artificial Intelligence

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Yogesh Kumar et al.

Summary: Artificial intelligence plays a significant role in disease diagnosis, drug discovery, and patient risk identification in healthcare. This article provides a comprehensive survey on the use of artificial intelligence techniques for diagnosing various diseases and compares the quality parameters of different studies.

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING (2022)

Review Urology & Nephrology

Artificial intelligence for the prediction of acute kidney injury during the perioperative period: systematic review and Meta-analysis of diagnostic test accuracy

Hanfei Zhang et al.

Summary: The systematic review and meta-analysis showed the promising performance of artificial intelligence in the early prediction of perioperative acute kidney injury. However, limitations such as lack of external validation and being conducted at a single center should be overcome.

BMC NEPHROLOGY (2022)

Article Chemistry, Physical

Prediction of Ecofriendly Concrete Compressive Strength Using Gradient Boosting Regression Tree Combined with GridSearchCV Hyperparameter-Optimization Techniques

Zaineb M. Alhakeem et al.

Summary: A hybrid model with optimization technique was used to predict the compressive strength of eco-friendly concrete. The results showed that the proposed model had high accuracy and generalization. Furthermore, the factors influencing the compressive strength were explained using the SHAP approach.

MATERIALS (2022)

Article Computer Science, Interdisciplinary Applications

Extracting spatial effects from machine learning model using local interpretation method: An example of SHAP and XGBoost

Ziqi Li

Summary: This paper introduces the local interpretation methods of machine learning models and demonstrates how to extract spatial effects using SHAP. Simulation experiments and empirical research show that locally interpreted machine learning models can be a good alternative to spatial statistical models and perform better in certain circumstances.

COMPUTERS ENVIRONMENT AND URBAN SYSTEMS (2022)

Article Computer Science, Artificial Intelligence

Precision-recall curve (PRC) classification trees

Jiaju Miao et al.

Summary: The classification of imbalanced data is a significant challenge for most classification algorithms, especially in applications like disease diagnosis and fraud detection. A novel tree-based algorithm, the PRC classification tree, was proposed based on the area under the precision-recall curve for variable selection, showing promising results for class-imbalanced data sets.

EVOLUTIONARY INTELLIGENCE (2022)

Article Medicine, General & Internal

Risk factors for acute kidney injury in critically ill patients with torso injury A retrospective observational single-center study

Young Hoon Sul et al.

Summary: Acute kidney injury (AKI) is common in trauma patients, with identified risk factors including bowel injury, cumulative fluid balance >2.5 L for 24 hours, lactate levels, and vasopressor use. AKI is associated with higher mortality in ICU patients with torso injuries, and recognizing risk factors early can aid in risk stratification and optimal ICU care.

MEDICINE (2021)

Review Biochemistry & Molecular Biology

How Machine Learning Will Transform Biomedicine

Jeremy Goecks et al.

Article Multidisciplinary Sciences

A clinically applicable approach to continuous prediction of future acute kidney injury

Nenad Tomasev et al.

NATURE (2019)

Editorial Material Urology & Nephrology

Artificial intelligence to predict AKI: is it a breakthrough?

John A. Kellum et al.

NATURE REVIEWS NEPHROLOGY (2019)

Review Urology & Nephrology

The Role of Risk Prediction Models in Prevention and Management of AKI

Luke E. Hodgson et al.

SEMINARS IN NEPHROLOGY (2019)

Review Urology & Nephrology

Novel acute kidney injury biomarkers: their characteristics, utility and concerns

Braian M. Beker et al.

INTERNATIONAL UROLOGY AND NEPHROLOGY (2018)

Article Computer Science, Artificial Intelligence

SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary

Alberto Fernandez et al.

JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH (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 Critical Care Medicine

Acute kidney injury following severe trauma: Risk factors and long-term outcome

Mikael Eriksson et al.

JOURNAL OF TRAUMA AND ACUTE CARE SURGERY (2015)

Article Critical Care Medicine

Acute kidney injury following severe trauma: Risk factors and long-term outcome

Mikael Eriksson et al.

JOURNAL OF TRAUMA AND ACUTE CARE SURGERY (2015)

Article Urology & Nephrology

Biomarkers in Acute Kidney Injury: Are We Ready for Prime Time?

Prasad Devarajan et al.

NEPHRON CLINICAL PRACTICE (2014)

Article Medicine, General & Internal

Urinary Biomarkers for Early Detection of Recovery in Patients with Acute Kidney Injury

Sung Jin Moon et al.

JOURNAL OF KOREAN MEDICAL SCIENCE (2013)