Artificial Neural Networks Applications in Dialysis

Fernández, Elmer; Valtuille, Rodolfo; Balzarini, Mónica

doi:10.1007/978-3-642-27558-6_8

Cited by 5 publications

(3 citation statements)

References 29 publications

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“…Patients’ quality of life is the most important concern for the newer designs of dialysis technique [1,242,243,244,245,246,247,248,249,250,251,252,253,254]. New developments that can facilitate the dialysis process for patients is a primary goal in the search of new filtering materials as well as measuring techniques to monitor water redistribution in the organism in the dialysis process–both cost-reducing and non-invasive methods.…”

Section: Discussion and Outlookmentioning

confidence: 99%

“…New developments that can facilitate the dialysis process for patients is a primary goal in the search of new filtering materials as well as measuring techniques to monitor water redistribution in the organism in the dialysis process–both cost-reducing and non-invasive methods. The development of feedback systems having the task of automatic control of dialysis and ultrafiltration process to optimize the treatment variables (in particular, the rate of ultrafiltration) requires sensor measurements [242,244,245,246] and computer modeling of the results [247].…”

Section: Discussion and Outlookmentioning

confidence: 99%

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Physical Processes in Polymeric Filters Used for Dialysis

et al. 2019

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The key physical processes in polymeric filters used for the blood purification include transport across the capillary wall and the interaction of blood cells with the polymer membrane surface. Theoretical modeling of membrane transport is an important tool which provides researchers with a quantification of the complex phenomena involved in dialysis. In the paper, we present a dense review of the most successful theoretical approaches to the description of transport across the polymeric membrane wall as well as the cell–polymer surface interaction, and refer to the corresponding experimental methods while studying these phenomena in dialyzing filters.

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Section: Discussion and Outlookmentioning

confidence: 99%

Section: Discussion and Outlookmentioning

confidence: 99%

Physical Processes in Polymeric Filters Used for Dialysis

et al. 2019

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“…Research in this area is still somehow disconnected and tentative. For instance, AI and ML have addressed problems concerning anemia and other issues in hemodialysis (HD) patients [20,21]. Artificial neural networks were used for predicting total body water in HD patients [22] and the target range of plasma intact parathyroid hormone concentration [23].…”

Section: Present Artificial Intelligence In Medicine: Challenges For mentioning

confidence: 99%

Artificial Intelligence for the Artificial Kidney: Pointers to the Future of a Personalized Hemodialysis Therapy

et al. 2018

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Background: Current dialysis devices are not able to react when unexpected changes occur during dialysis treatment or to learn about experience for therapy personalization. Furthermore, great efforts are dedicated to develop miniaturized artificial kidneys to achieve a continuous and personalized dialysis therapy, in order to improve the patient’s quality of life. These innovative dialysis devices will require a real-time monitoring of equipment alarms, dialysis parameters, and patient-related data to ensure patient safety and to allow instantaneous changes of the dialysis prescription for the assessment of their adequacy. The analysis and evaluation of the resulting large-scale data sets enters the realm of “big data” and will require real-time predictive models. These may come from the fields of machine learning and computational intelligence, both included in artificial intelligence, a branch of engineering involved with the creation of devices that simulate intelligent behavior. The incorporation of artificial intelligence should provide a fully new approach to data analysis, enabling future advances in personalized dialysis therapies. With the purpose to learn about the present and potential future impact on medicine from experts in artificial intelligence and machine learning, a scientific meeting was organized in the Hospital Universitari Bellvitge (L’Hospitalet, Barcelona). As an outcome of that meeting, the aim of this review is to investigate artificial intel ligence experiences on dialysis, with a focus on potential barriers, challenges, and prospects for future applications of these technologies. Summary and Key Messages: Artificial intelligence research on dialysis is still in an early stage, and the main challenge relies on interpretability and/or comprehensibility of data models when applied to decision making. Artificial neural networks and medical decision support systems have been used to make predictions about anemia, total body water, or intradialysis hypotension and are promising approaches for the prescription and monitoring of hemodialysis therapy. Current dialysis machines are continuously improving due to innovative technological developments, but patient safety is still a key challenge. Real-time monitoring systems, coupled with automatic instantaneous biofeedback, will allow changing dialysis prescriptions continuously. The integration of vital sign monitoring with dialysis parameters will produce large data sets that will require the use of data analysis techniques, possibly from the area of machine learning, in order to make better decisions and increase the safety of patients.

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