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

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          Abstract

          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 care unit), and to compare its performance with that of three other machine learning models.

          Methods

          We used the eICU Collaborative Research Database (eICU-CRD) for model development and performance comparison. The prediction performance of the XGBoot model was compared with the other three machine learning models. These models included LR (logistic regression), SVM (support vector machines), and RF (random forest). In the model comparison, the AUROC (area under receiver operating curve), accuracy, precision, recall, and F1 score were used to evaluate the predictive performance of each model.

          Results

          A total of 7548 AKI patients were analyzed in this study. The overall in-hospital mortality of AKI patients was 16.35%. 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). The precision (0.860) and recall (0.994) of the XGBoost model rank second among the four models.

          Conclusion

          XGBoot model had obvious advantages of performance compared to the other machine learning models. This will be helpful for risk identification and early intervention for AKI patients at risk of death.

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          Most cited references32

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          A comparison of methods for multiclass support vector machines.

          Support vector machines (SVMs) were originally designed for binary classification. How to effectively extend it for multiclass classification is still an ongoing research issue. Several methods have been proposed where typically we construct a multiclass classifier by combining several binary classifiers. Some authors also proposed methods that consider all classes at once. As it is computationally more expensive to solve multiclass problems, comparisons of these methods using large-scale problems have not been seriously conducted. Especially for methods solving multiclass SVM in one step, a much larger optimization problem is required so up to now experiments are limited to small data sets. In this paper we give decomposition implementations for two such "all-together" methods. We then compare their performance with three methods based on binary classifications: "one-against-all," "one-against-one," and directed acyclic graph SVM (DAGSVM). Our experiments indicate that the "one-against-one" and DAG methods are more suitable for practical use than the other methods. Results also show that for large problems methods by considering all data at once in general need fewer support vectors.
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            A systematic analysis of performance measures for classification tasks

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              • Article: not found

              Global epidemiology and outcomes of acute kidney injury

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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                4 February 2021
                2021
                : 16
                : 2
                : e0246306
                Affiliations
                [1 ] Information Center, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
                [2 ] Department of Medical Informatics, West China Medical School, Chengdu, Sichuan Province, China
                [3 ] School of Life Science & Technology, University of Electronic Science & Technology of China, Chengdu, Sichuan Province, China
                [4 ] Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States of America
                BronxCare Health System, Affiliated with Icahn School of Medicine at Mount Sinai, NY, USA, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0002-1369-4625
                Article
                PONE-D-20-27009
                10.1371/journal.pone.0246306
                7861386
                33539390
                687608dc-e917-4fb0-83e3-719f2bbc8128
                © 2021 Liu et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 27 August 2020
                : 17 January 2021
                Page count
                Figures: 3, Tables: 4, Pages: 11
                Funding
                Funded by: Special project of central government guiding local science and technology development
                Award ID: 2020ZYD001
                Award Recipient :
                Funded by: Sichuan Science and Technology Program
                Award ID: 2020YFS0162
                Award Recipient :
                Funded by: Sichuan Science and technology support plan project
                Award ID: 2019JDPT0008
                Award Recipient :
                Jialin Liu, Sichuan Science and Technology Program under Grant No. 2020YFS0162. Ke Li,Special project of central government guiding local science and technology development under Grant No.2020ZYD001.Sichuan Science and technology support plan project NO.2019JDPT0008. The funders had a role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Computer and Information Sciences
                Artificial Intelligence
                Machine Learning
                Biology and Life Sciences
                Population Biology
                Population Metrics
                Death Rates
                Physical Sciences
                Mathematics
                Applied Mathematics
                Algorithms
                Machine Learning Algorithms
                Research and Analysis Methods
                Simulation and Modeling
                Algorithms
                Machine Learning Algorithms
                Computer and Information Sciences
                Artificial Intelligence
                Machine Learning
                Machine Learning Algorithms
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Forecasting
                Physical Sciences
                Mathematics
                Statistics
                Statistical Methods
                Forecasting
                Medicine and Health Sciences
                Epidemiology
                Medical Risk Factors
                Physical Sciences
                Mathematics
                Applied Mathematics
                Algorithms
                Machine Learning Algorithms
                Boosting Algorithms
                Research and Analysis Methods
                Simulation and Modeling
                Algorithms
                Machine Learning Algorithms
                Boosting Algorithms
                Computer and Information Sciences
                Artificial Intelligence
                Machine Learning
                Machine Learning Algorithms
                Boosting Algorithms
                Engineering and Technology
                Management Engineering
                Decision Analysis
                Decision Trees
                Research and Analysis Methods
                Decision Analysis
                Decision Trees
                Biology and Life Sciences
                Biochemistry
                Biomarkers
                Creatinine
                Custom metadata
                The eICU Collaborative Research Database is a third party data, which is a multi-center intensive care unit database with high granularity data for over 200,000 admissions to ICUs monitored by eICU Programs across the United States. We did not have any special access privileges. Other researchers could also access this database in the same manner. Details of the data access process are available online ( https://eicu-crd.mit.edu). Use of the data requires proof of completion of the CITI “Data or Specimens Only Research” course ( https://www.citiprogram.org/index.cfm?pageID=154&icat=0&ac=0) and signing of a data use agreement mandating responsible handling of the data and adhering to the principle of collaborative research. Once approved, data can be directly downloaded from the eICU Collaborative Research Database project on PhysioNet ( https://physionet.org/login/).

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