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      Feature Ranking in Predictive Models for Hospital-Acquired Acute Kidney Injury

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          Abstract

          Acute Kidney Injury (AKI) is a common complication encountered among hospitalized patients, imposing significantly increased cost, morbidity, and mortality. Early prediction of AKI has profound clinical implications because currently no treatment exists for AKI once it develops. Feature selection (FS) is an essential process for building accurate and interpretable prediction models, but to our best knowledge no study has investigated the robustness and applicability of such selection process for AKI. In this study, we compared eight widely-applied FS methods for AKI prediction using nine-years of electronic medical records (EMR) and examined heterogeneity in feature rankings produced by the methods. FS methods were compared in terms of stability with respect to data sampling variation, similarity between selection results, and AKI prediction performance. Prediction accuracy did not intrinsically guarantee the feature ranking stability. Across different FS methods, the prediction performance did not change significantly, while the importance rankings of features were quite different. A positive correlation was observed between the complexity of suitable FS method and sample size. This study provides several practical implications, including recognizing the importance of feature stability as it is desirable for model reproducibility, identifying important AKI risk factors for further investigation, and facilitating early prediction of AKI.

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          Random forests

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            Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy.

            Feature selection is an important problem for pattern classification systems. We study how to select good features according to the maximal statistical dependency criterion based on mutual information. Because of the difficulty in directly implementing the maximal dependency condition, we first derive an equivalent form, called minimal-redundancy-maximal-relevance criterion (mRMR), for first-order incremental feature selection. Then, we present a two-stage feature selection algorithm by combining mRMR and other more sophisticated feature selectors (e.g., wrappers). This allows us to select a compact set of superior features at very low cost. We perform extensive experimental comparison of our algorithm and other methods using three different classifiers (naive Bayes, support vector machine, and linear discriminate analysis) and four different data sets (handwritten digits, arrhythmia, NCI cancer cell lines, and lymphoma tissues). The results confirm that mRMR leads to promising improvement on feature selection and classification accuracy.
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              machine.

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                Author and article information

                Contributors
                henryhu200211@163.com
                meiliu@kumc.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                23 November 2018
                23 November 2018
                2018
                : 8
                Affiliations
                [1 ]ISNI 0000 0004 1790 3548, GRID grid.258164.c, Big Data Decision Institute (BDDI), , Jinan University, ; Guangzhou, 510632 China
                [2 ]Guangdong Engineering Technology Research Center for Big Data Precision Healthcare, Guangzhou, 510632 China
                [3 ]ISNI 0000 0001 2177 6375, GRID grid.412016.0, Division of Nephrology and Hypertension and the Kidney Institute, , University of Kansas Medical Center, ; Kansas City, 66160 USA
                [4 ]ISNI 0000 0004 1936 9000, GRID grid.21925.3d, Center for Critical Care Nephrology, Department of Critical Care Medicine, , University of Pittsburgh School of Medicine, ; Pittsburgh, 15260 USA
                [5 ]ISNI 0000 0001 2177 6375, GRID grid.412016.0, Department of Internal Medicine, Division of Medical Informatics, , University of Kansas Medical Center, ; Kansas City, 66160 USA
                35487
                10.1038/s41598-018-35487-0
                6251919
                30470779
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 91746204
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100007162, Guangdong Science and Technology Department (Science and Technology Department, Guangdong Province);
                Award ID: 2017B030308008
                Award Recipient :
                Funded by: Guangdong Engineering Technology Research Center for Big Data Precision Healthcare (Grant No.603141789047) Fundamental Research Funds for the Central Universities (Grant No.21618315)
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