Neuro-Fuzzy Technology as a Predictor of Parathyroid Hormone Level in Hemodialysis Patients

Chen, Chiou An; Li, Yu-Chuan; Lin, Yuh Feng; Yu, Fei; Huang, Weijie; Chiu, Jainn Shiun

doi:10.1620/tjem.211.81

Cited by 6 publications

(2 citation statements)

References 20 publications

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“…This algorithm provided excellent discrimination (AUROC = 0:83, p = 0:003) [36]. Usually, frequent measurement is needed to avoid inadequate prescription of phosphate binders and vitamin D. AI can repeatedly perform the forecasting tasks and may be a satisfactory substitute for laboratory tests [37]. NNs were used to predict HD patients who need more frequent vitamin D dosage, using only simple clinical parameters [38].…”

Section: Biomed Research Internationalmentioning

confidence: 99%

Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review

Burlacu

Iftene

Jugrin

et al. 2020

BioMed Research International

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Background. The purpose of this review is to depict current research and impact of artificial intelligence/machine learning (AI/ML) algorithms on dialysis and kidney transplantation. Published studies were presented from two points of view: What medical aspects were covered? What AI/ML algorithms have been used? Methods. We searched four electronic databases or studies that used AI/ML in hemodialysis (HD), peritoneal dialysis (PD), and kidney transplantation (KT). Sixty-nine studies were split into three categories: AI/ML and HD, PD, and KT, respectively. We identified 43 trials in the first group, 8 in the second, and 18 in the third. Then, studies were classified according to the type of algorithm. Results. AI and HD trials covered: (a) dialysis service management, (b) dialysis procedure, (c) anemia management, (d) hormonal/dietary issues, and (e) arteriovenous fistula assessment. PD studies were divided into (a) peritoneal technique issues, (b) infections, and (c) cardiovascular event prediction. AI in transplantation studies were allocated into (a) management systems (ML used as pretransplant organ-matching tools), (b) predicting graft rejection, (c) tacrolimus therapy modulation, and (d) dietary issues. Conclusions. Although guidelines are reluctant to recommend AI implementation in daily practice, there is plenty of evidence that AI/ML algorithms can predict better than nephrologists: volumes, Kt/V, and hypotension or cardiovascular events during dialysis. Altogether, these trials report a robust impact of AI/ML on quality of life and survival in G5D/T patients. In the coming years, one would probably witness the emergence of AI/ML devices that facilitate the management of dialysis patients, thus increasing the quality of life and survival.

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Section: Biomed Research Internationalmentioning

confidence: 99%

Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review

Burlacu

Iftene

Jugrin

et al. 2020

BioMed Research International

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“…The mark sign of "√" is used to show where there exist tapped areas and the null shows that there is need for more attention from researchers. [75], [97], [113], [178], [197], [221], [222] [139], [152], [154], [156], [173], [208] [64], [93], [120], [131], [183] 5 Data mining [43], [65], [66], [116], [148], [160], [166], [188], [228] [74], [104], [123], [146], [206], [211] [35], [68], [103], [121], [142], [148], [161], [184], [186], [190], [216] 13…”

Section: Applications Of Soft Computingmentioning

confidence: 99%

A Holistic Review of Soft Computing Techniques

Philip¹,

Mom²,

Igwue³

2017

ACM

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Due to notable technological convergence that brought about exponential growth in computer world, SoftComputing (SC) has played a vital role with automation capability features to new levels of complex applications. In this research paper, the authors reviewed journals related to the subject matter with the aim of striking a convincing balance between a system that is capable of tolerance to uncertainty, imprecision, approximate reasoning and partial truth to achieve tractability, robustness, economy of communication, high machine intelligence quotient (MIQ), low cost solution and better rapport with reality to conventional techniques. This paper gives an insight on four major consortiums of SC that sprang from the concept of cybernetics, explores and reviews the different techniques, methodologies; application areas and algorithms are formulated to give an idea on how these computing techniques are applied to create intelligent agents to solve a variety of problems. The mechanisms highlighted can serve as an inspiration platform and awareness to new and old researchers that are not or fully grounded in this unique area of research and to create avenue in order to fully embrace the techniques in research communities.

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