Theranostic roles of machine learning in clinical management of kidney stone disease
Kidney stone disease (KSD) is a common illness caused by deposition of solid minerals formed inside the kidney. The disease prevalence varies, based on sociodemographic, lifestyle, dietary, genetic, gender, age, environmental and climatic factors, but has been continuously increasing worldwide. KSD...
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th-mahidol.818052023-05-19T14:40:09Z Theranostic roles of machine learning in clinical management of kidney stone disease Sassanarakkit S. Mahidol University Computer Science Kidney stone disease (KSD) is a common illness caused by deposition of solid minerals formed inside the kidney. The disease prevalence varies, based on sociodemographic, lifestyle, dietary, genetic, gender, age, environmental and climatic factors, but has been continuously increasing worldwide. KSD is a highly recurrent disease, and the recurrence rate is about 11% within two years after the stone removal. Recently, machine learning has been widely used for KSD detection, stone type prediction, determination of appropriate treatment modality and prediction of therapeutic outcome. This review provides a brief overview of KSD and discusses how machine learning can be applied to diagnostics, therapeutics and prognostics in clinical management of KSD for better therapeutic outcome. 2023-05-19T07:40:09Z 2023-05-19T07:40:09Z 2023-01-01 Review Computational and Structural Biotechnology Journal Vol.21 (2023) , 260-266 10.1016/j.csbj.2022.12.004 20010370 2-s2.0-85143660049 https://repository.li.mahidol.ac.th/handle/123456789/81805 SCOPUS |
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Computer Science Sassanarakkit S. Theranostic roles of machine learning in clinical management of kidney stone disease |
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Kidney stone disease (KSD) is a common illness caused by deposition of solid minerals formed inside the kidney. The disease prevalence varies, based on sociodemographic, lifestyle, dietary, genetic, gender, age, environmental and climatic factors, but has been continuously increasing worldwide. KSD is a highly recurrent disease, and the recurrence rate is about 11% within two years after the stone removal. Recently, machine learning has been widely used for KSD detection, stone type prediction, determination of appropriate treatment modality and prediction of therapeutic outcome. This review provides a brief overview of KSD and discusses how machine learning can be applied to diagnostics, therapeutics and prognostics in clinical management of KSD for better therapeutic outcome. |
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Mahidol University |
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Mahidol University Sassanarakkit S. |
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Review |
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Sassanarakkit S. |
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Sassanarakkit S. |
title |
Theranostic roles of machine learning in clinical management of kidney stone disease |
title_short |
Theranostic roles of machine learning in clinical management of kidney stone disease |
title_full |
Theranostic roles of machine learning in clinical management of kidney stone disease |
title_fullStr |
Theranostic roles of machine learning in clinical management of kidney stone disease |
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Theranostic roles of machine learning in clinical management of kidney stone disease |
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theranostic roles of machine learning in clinical management of kidney stone disease |
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2023 |
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https://repository.li.mahidol.ac.th/handle/123456789/81805 |
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