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Comparison of Machine Learning and Logistic Regression Models in Predicting Acute Kidney Injury: A systematic review and meta-analysis

Xuan Song, Xinyan Liu, Fei Liu, Chunting Wang
2021 International Journal of Medical Informatics  
We aimed to assess whether machine learning models are superior at predicting acute kidney injury (AKI) compared to logistic regression (LR), a conventional prediction model.  ...  However, specific ML models, such as gradient boosting (0.838 ± 0.077), exhibited superior performance at predicting AKI as compared to other ML models in the literature (p < 0.05).  ...  What was already known on the topic • Logistic regression has been used to predict acute kidney injury in critical care settings. • Interest in machine learning algorithms to predict acute kidney injury  ... 
doi:10.1016/j.ijmedinf.2021.104484 pmid:33991886 fatcat:uegdxopmrbbbta7ozqaadk7lcu

Prediction Models for AKI in ICU: A Comparative Study

Qing Qian, Jinming Wu, Jiayang Wang, Haixia Sun, Lei Yang
2021 International Journal of General Medicine  
To assess the performance of models for early prediction of acute kidney injury (AKI) in the Intensive Care Unit (ICU) setting.  ...  Six models from three types of methods were tested: Logistic Regression (LR), Support Vector Machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Decision Machine  ...  Acknowledgments The authors thank Dr Jiao Li (Chinese Academy of Medical Sciences & Peking Union Medical College) for helpful comments on the manuscript.  ... 
doi:10.2147/ijgm.s289671 pmid:33664585 pmcid:PMC7921629 fatcat:2cqwxusa5fcanfkkd77nnxwufi

Predicting mortality of patients with acute kidney injury in the ICU using XGBoost model

Jialin Liu, Jinfa Wu, Siru Liu, Mengdie Li, Kunchang Hu, Ke Li, Muhammad Adrish
2021 PLoS ONE  
Purpose The goal of this study is to construct a mortality prediction model using the XGBoot (eXtreme Gradient Boosting) decision tree model for AKI (acute kidney injury) patients in the ICU (intensive  ...  The best performing algorithm in this study was XGBoost with the highest AUROC (0.796, p < 0.01), F1(0.922, p < 0.01) and accuracy (0.860).  ...  XGBoost is an improved algorithm based on the gradient boosting decision tree, which can efficiently construct boosted trees and run in parallel.  ... 
doi:10.1371/journal.pone.0246306 pmid:33539390 fatcat:iys3uropjfdrziduoorbia2myi

Applications of Machine Learning Approaches in Emergency Medicine; a Review Article

Negin Shafaf, Hamed Malek
2019 Archives of Academic Emergency Medicine  
In each of these categories, the most important studies have been chosen and accuracy and results of the algorithms have been briefly evaluated by mentioning machine learning techniques and used datasets  ...  These studies belonged to three categories: prediction and detection of disease; prediction of need for admission, discharge and also mortality; and machine learning based triage systems.  ...  Acute kidney injury Acute kidney injury (AKI) is a disease that can occur in a few hours to a few days and can lead to kidney failure if not managed properly and the patients would need dialysis for the  ... 
pmid:31555764 pmcid:PMC6732202 fatcat:zjd3l57eufbrnotveb2adnincu

Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients

Antonin Dauvin, Carolina Donado, Patrik Bachtiger, Ke-Chun Huang, Christopher Martin Sauer, Daniele Ramazzotti, Matteo Bonvini, Leo Anthony Celi, Molly J. Douglas
2019 npj Digital Medicine  
significantly better at predicting baseline hemoglobin value in patients with admission hemoglobin of <10 g/dl.  ...  Compared to assuming the baseline to be the same as the admission lab value, machine learning performed significantly better at classifying acute kidney injury regardless of initial creatinine value, and  ...  ACKNOWLEDGEMENTS Software: This research made use of the community-developed statistical software R, 43 community-developed core Python and Julia packages including IPython, 49 Matplotlib, 50 Scikit-learn  ... 
doi:10.1038/s41746-019-0192-z pmid:31815192 pmcid:PMC6884624 fatcat:wez5jopmc5dopmnsofw4gp54e4

Artificial Intelligence for Risk Prediction of Rehospitalization with Acute Kidney Injury in Sepsis Survivors

Shuo-Ming Ou, Kuo-Hua Lee, Ming-Tsun Tsai, Wei-Cheng Tseng, Yuan-Chia Chu, Der-Cherng Tarng
2022 Journal of Personalized Medicine  
We adopted a machine learning method by using models based on logistic regression, random forest, extra tree classifier, gradient boosting decision tree (GBDT), extreme gradient boosting, and light gradient  ...  Sepsis survivors have a higher risk of long-term complications. Acute kidney injury (AKI) may still be common among sepsis survivors after discharge from sepsis.  ...  Conflicts of Interest: All listed authors have no conflict of interest to declare.  ... 
doi:10.3390/jpm12010043 pmid:35055358 pmcid:PMC8777885 fatcat:zmt4q6zwmnblfj4fdbf7vakhfe

Machine learning in perioperative medicine: a systematic review

Valentina Bellini, Marina Valente, Giorgia Bertorelli, Barbara Pifferi, Michelangelo Craca, Monica Mordonini, Gianfranco Lombardo, Eleonora Bottani, Paolo Del Rio, Elena Bignami
2022 Journal of Anesthesia, Analgesia and Critical Care  
Results The most considered outcomes were mortality risk, systemic complications (pulmonary, cardiovascular, acute kidney injury (AKI), etc.), ICU admission, anesthesiologic risk and prolonged length of  ...  Models with best performance were random forest and gradient boosting, with AUC > 0.90. Conclusions The application of ML in medicine appears to have a great potential.  ...  cardiac operative risk evaluation (EuroSCORE) [5] or the General Surgery Acute Kidney Injury Risk Index Classification System [6] .  ... 
doi:10.1186/s44158-022-00033-y fatcat:lcwvbpnbgjcmdb465rvs3yfkl4

Artificial Intelligence in Acute Kidney Injury Risk Prediction

Joana Gameiro, Tiago Branco, José António Lopes
2020 Journal of Clinical Medicine  
Acute kidney injury (AKI) is a frequent complication in hospitalized patients, which is associated with worse short and long-term outcomes.  ...  The advances in clinical informatics and the increasing availability of electronic medical records have allowed for the development of artificial intelligence predictive models of risk estimation in AKI  ...  Conflicts of Interest: There is no conflict of interest. The results presented in this paper have not been published previously in whole or part.  ... 
doi:10.3390/jcm9030678 pmid:32138284 pmcid:PMC7141311 fatcat:tjyaglaagrewpg2brwonziui74

Prediction of Acute Kidney Injury With a Machine Learning Algorithm Using Electronic Health Record Data

Hamid Mohamadlou, Anna Lynn-Palevsky, Christopher Barton, Uli Chettipally, Lisa Shieh, Jacob Calvert, Nicholas R. Saber, Ritankar Das
2018 Canadian Journal of Kidney Health and Disease  
A major problem in treating acute kidney injury (AKI) is that clinical criteria for recognition are markers of established kidney damage or impaired function; treatment before such damage manifests is  ...  Design: We used a machine learning technique, boosted ensembles of decision trees, to train an AKI prediction tool on retrospective data taken from more than 300 000 inpatient encounters.  ...  : Mohamadlou, Barton, Calvert, Lynn-Palevsky, Saber, and Das are employees of Dascena, developers of the predictive algorithm.  ... 
doi:10.1177/2054358118776326 pmid:30094049 pmcid:PMC6080076 fatcat:4ktodzk5qvew3kijo6r6rfpcii

Parsimonious Machine Learning Models to Predict Resource Use in Cardiac Surgery Across a Statewide Collaborative

Arjun Verma, Yas Sanaiha, Joseph Hadaya, Anthony Jason Maltagliati, Zachary Tran, Ramin Ramezani, Richard J. Shemin, Peyman Benharash
2022 JTCVS Open  
This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article.  ...  of record.  ...  Mortality at 30-days, acute kidney injury (AKI), postoperative blood transfusion, reoperation and ICU LOS were also considered.  ... 
doi:10.1016/j.xjon.2022.04.017 fatcat:kvfoqmaeqrf6jer4y7zwehrn54

Artificial Intelligence for Risk Prediction of End-Stage Renal Disease in Sepsis Survivors with Chronic Kidney Disease

Kuo-Hua Lee, Yuan-Chia Chu, Ming-Tsun Tsai, Wei-Cheng Tseng, Yao-Ping Lin, Shuo-Ming Ou, Der-Cherng Tarng
2022 Biomedicines  
We adopted the random forest, extra trees, extreme gradient boosting, light gradient boosting machine (LGBM), and gradient boosting decision tree (GBDT) algorithms to predict the risk of ESRD development  ...  Sepsis may lead to kidney function decline in patients with chronic kidney disease (CKD), and the deleterious effect may persist in patients who survive sepsis.  ...  ), and gradient boosting decision tree (GBDT), to predict ESRD after surviving sepsis.  ... 
doi:10.3390/biomedicines10030546 pmid:35327348 pmcid:PMC8945427 fatcat:i2fzrne7tjb7hddxxsbvkhjnru

The prediction of mortality influential variables in an intensive care unit: a case study

Naghmeh Khajehali, Zohreh Khajehali, Mohammad Jafar Tarokh
2021 Personal and Ubiquitous Computing  
Generally, in the health care systems, having a fast and precise ICU mortality prediction for patients plays a key role in care quality, resulting in reduced costs and improved survival chances of the  ...  Besides, machine learning models are employed to predict the risk of mortality ICU discharge.  ...  Lin et al.(2019) [37] compared RF with other machine learning algorithms, including ANN and SVM models, and customized SAPS II model for acute kidney injury (AKI) patients in the ICU to obtain the best  ... 
doi:10.1007/s00779-021-01540-5 pmid:33654479 pmcid:PMC7907311 fatcat:3vk226x6hjgb3js75oo3e5zhje

Does Artificial Intelligence Make Clinical Decision Better? A Review of Artificial Intelligence and Machine Learning in Acute Kidney Injury Prediction

Tao Han Lee, Jia-Jin Chen, Chi-Tung Cheng, Chih-Hsiang Chang
2021 Healthcare  
Acute kidney injury (AKI) is a common complication of hospitalization that greatly and negatively affects the short-term and long-term outcomes of patients.  ...  Our review begins with the background and history of the definition of AKI, and the evolution of AKI risk factors and prediction models is also appraised.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/healthcare9121662 pmid:34946388 pmcid:PMC8701097 fatcat:2q646jb2arcx7neg4ig2h2d3aa

Application of Machine Learning to Predict Acute Kidney Disease in Patients With Sepsis Associated Acute Kidney Injury

Jiawei He, Jin Lin, Meili Duan
2021 Frontiers in Medicine  
Sepsis-associated acute kidney injury (AKI) is frequent in patients admitted to intensive care units (ICU) and may contribute to adverse short-term and long-term outcomes.  ...  We aimed to develop and validate machine learning models to predict the occurrence of AKD in patients with sepsis-associated AKI.Methods: Using clinical data from patients with sepsis in the ICU at Beijing  ...  Exploiting the cost (In) sensitivity of decision tree responsiveness in patients with oliguric acute kidney injury in critical care. splitting criteria. ICML. (2000) 1:8.  ... 
doi:10.3389/fmed.2021.792974 pmid:34957162 pmcid:PMC8703139 fatcat:4jvbwvlfovcuhkpdgicsxno6je

COVID-Net Biochem: An Explainability-driven Framework to Building Machine Learning Models for Predicting Survival and Kidney Injury of COVID-19 Patients from Clinical and Biochemistry Data [article]

Hossein Aboutalebi, Maya Pavlova, Mohammad Javad Shafiee, Adrian Florea, Andrew Hryniowski, Alexander Wong
2022 arXiv   pre-print
A collection of different machine learning models with a diversity of gradient based boosting tree architectures and deep transformer architectures was designed and trained specifically for survival and  ...  kidney injury prediction based on the carefully selected clinical and biochemical markers.  ...  The gradient boosting algorithms rely on creating and learning an ensemble of weak prediction models (decision trees) by minimizing an arbitrary differentiable loss function.  ... 
arXiv:2204.11210v1 fatcat:dytw5sucjjhszjrcbevb4p4vyy
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