Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches
Cardiac surgery-associated AKI (CSA-AKI) is common after cardiac surgery and has an adverse impact on short- and long-term mortality. Early identification of patients at high risk of CSA-AKI by applying risk prediction models allows clinicians to closely monitor these patients and initiate effective...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Journal of Clinical Medicine |
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| Online Access: | https://www.mdpi.com/2077-0383/9/6/1767 |
| _version_ | 1797565945163546624 |
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| author | Charat Thongprayoon Panupong Hansrivijit Tarun Bathini Saraschandra Vallabhajosyula Poemlarp Mekraksakit Wisit Kaewput Wisit Cheungpasitporn |
| author_facet | Charat Thongprayoon Panupong Hansrivijit Tarun Bathini Saraschandra Vallabhajosyula Poemlarp Mekraksakit Wisit Kaewput Wisit Cheungpasitporn |
| author_sort | Charat Thongprayoon |
| collection | DOAJ |
| description | Cardiac surgery-associated AKI (CSA-AKI) is common after cardiac surgery and has an adverse impact on short- and long-term mortality. Early identification of patients at high risk of CSA-AKI by applying risk prediction models allows clinicians to closely monitor these patients and initiate effective preventive and therapeutic approaches to lessen the incidence of AKI. Several risk prediction models and risk assessment scores have been developed for CSA-AKI. However, the definition of AKI and the variables utilized in these risk scores differ, making general utility complex. Recently, the utility of artificial intelligence coupled with machine learning, has generated much interest and many studies in clinical medicine, including CSA-AKI. In this article, we discussed the evolution of models established by machine learning approaches to predict CSA-AKI. |
| first_indexed | 2024-03-10T19:20:00Z |
| format | Article |
| id | doaj.art-8d127a8ba94b43f89667ca6a5c6b3987 |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-10T19:20:00Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-8d127a8ba94b43f89667ca6a5c6b39872023-11-20T03:05:57ZengMDPI AGJournal of Clinical Medicine2077-03832020-06-0196176710.3390/jcm9061767Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning ApproachesCharat Thongprayoon0Panupong Hansrivijit1Tarun Bathini2Saraschandra Vallabhajosyula3Poemlarp Mekraksakit4Wisit Kaewput5Wisit Cheungpasitporn6Division of Nephrology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USADepartment of Internal Medicine, University of Pittsburgh Medical Center Pinnacle, Harrisburg, PA 17105, USADepartment of Internal Medicine, University of Arizona, Tucson, AZ 85724, USADepartment of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USADepartment of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79424, USADepartment of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok 10400, ThailandDivision of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USACardiac surgery-associated AKI (CSA-AKI) is common after cardiac surgery and has an adverse impact on short- and long-term mortality. Early identification of patients at high risk of CSA-AKI by applying risk prediction models allows clinicians to closely monitor these patients and initiate effective preventive and therapeutic approaches to lessen the incidence of AKI. Several risk prediction models and risk assessment scores have been developed for CSA-AKI. However, the definition of AKI and the variables utilized in these risk scores differ, making general utility complex. Recently, the utility of artificial intelligence coupled with machine learning, has generated much interest and many studies in clinical medicine, including CSA-AKI. In this article, we discussed the evolution of models established by machine learning approaches to predict CSA-AKI.https://www.mdpi.com/2077-0383/9/6/1767acute kidney injuryAKIcardiac surgerymachine learningartificial intelligencenephrology |
| spellingShingle | Charat Thongprayoon Panupong Hansrivijit Tarun Bathini Saraschandra Vallabhajosyula Poemlarp Mekraksakit Wisit Kaewput Wisit Cheungpasitporn Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches Journal of Clinical Medicine acute kidney injury AKI cardiac surgery machine learning artificial intelligence nephrology |
| title | Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches |
| title_full | Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches |
| title_fullStr | Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches |
| title_full_unstemmed | Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches |
| title_short | Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches |
| title_sort | predicting acute kidney injury after cardiac surgery by machine learning approaches |
| topic | acute kidney injury AKI cardiac surgery machine learning artificial intelligence nephrology |
| url | https://www.mdpi.com/2077-0383/9/6/1767 |
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