2022
DOI: 10.1111/hdi.13033
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Artificial intelligence and digital health for volume maintenance in hemodialysis patients

Abstract: Chronic fluid overload is associated with morbidity and mortality in hemodialysis patients. Optimizing the diagnosis and treatment of fluid overload remains a priority for the nephrology community. Although current methods of assessing fluid status, such as bioimpedance and lung ultrasound, have prognostic and diagnostic value, no single system or technique can be used to maintain euvolemia. The difficulty in maintaining and assessing fluid status led to a publication by the Kidney Health Initiative in 2019 ai… Show more

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Cited by 10 publications
(2 citation statements)
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References 73 publications
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“…9 Different approaches are currently studied to improve the fluid management of HD patients, which include biosensors, lung ultrasound, but also machine learning (ML) models. 10 Lee et al developed a recurrent neural network (RNN) based on time-invariant and timevarying features, which is able to predict intradialytic hypotension (IDH) with high accuracy, ranging from AUC-ROC 0.79 -0.94, depending on the definition of IDH. 11 Barbieri et al used an artificial neural network to estimate the minimum systolic blood pressure (SBP), postdialysis heart rate, weight, and Kt/V, while Chaudhuri et al predicted relative blood volume decrease of > 6.5% based on optical sensor data.…”
Section: Introductionmentioning
confidence: 99%
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“…9 Different approaches are currently studied to improve the fluid management of HD patients, which include biosensors, lung ultrasound, but also machine learning (ML) models. 10 Lee et al developed a recurrent neural network (RNN) based on time-invariant and timevarying features, which is able to predict intradialytic hypotension (IDH) with high accuracy, ranging from AUC-ROC 0.79 -0.94, depending on the definition of IDH. 11 Barbieri et al used an artificial neural network to estimate the minimum systolic blood pressure (SBP), postdialysis heart rate, weight, and Kt/V, while Chaudhuri et al predicted relative blood volume decrease of > 6.5% based on optical sensor data.…”
Section: Introductionmentioning
confidence: 99%
“…9 Different approaches are currently studied to improve the fluid management of HD patients, which include biosensors, lung ultrasound, but also machine learning (ML) models. 10…”
Section: Introductionmentioning
confidence: 99%