Literature DB >> 30082061

Using Technology to Inform and Deliver Precise Personalized Care to Patients With End-Stage Kidney Disease.

Len Usvyat1, Lorien S Dalrymple2, Franklin W Maddux2.   

Abstract

Consistent with the increase of precision medicine, the care of patients with end-stage kidney disease (ESKD) requiring maintenance dialysis therapy should evolve to become more personalized. Precise and personalized care is nuanced and informed by a number of factors including an individual's needs and preferences, disease progression, and response to and tolerance of treatments. Technology can support the delivery of more precise and personalized care through multiple mechanisms, including more accurate and real-time assessments of key care elements, enhanced treatment monitoring, and remote monitoring of home dialysis therapies. Data from health care and non-health care sources and advanced analytical methods such as machine learning can be used to create novel insights, and large volumes of data can be integrated to support clinical decisions. Health care models continue to evolve and the opportunities and need for novel care approaches supported by technology and health informatics continue to expand as the delivery and organization of health care changes. Ultimately, precise personalized care for ESKD, including dialysis therapy, will become more feasible as the biological, social, and environmental determinants of health are more broadly understood and as advances in science, engineering, and information management create the means to provide truly precise care for ESKD.
Copyright © 2018 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Technology; artificial intelligence; dialysis; individualized medicine; precise personalized care; renaldisease

Mesh:

Year:  2018        PMID: 30082061     DOI: 10.1016/j.semnephrol.2018.05.011

Source DB:  PubMed          Journal:  Semin Nephrol        ISSN: 0270-9295            Impact factor:   5.299


  5 in total

1.  Hierarchical Clustering Analysis for Predicting 1-Year Mortality After Starting Hemodialysis.

Authors:  Yohei Komaru; Teruhiko Yoshida; Yoshifumi Hamasaki; Masaomi Nangaku; Kent Doi
Journal:  Kidney Int Rep       Date:  2020-05-23

Review 2.  Leveraging Data Science for a Personalized Haemodialysis.

Authors:  Miguel Hueso; Lluís de Haro; Jordi Calabia; Rafael Dal-Ré; Cristian Tebé; Karina Gibert; Josep M Cruzado; Alfredo Vellido
Journal:  Kidney Dis (Basel)       Date:  2020-05-25

Review 3.  Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review.

Authors:  Alexandru Burlacu; Adrian Iftene; Daniel Jugrin; Iolanda Valentina Popa; Paula Madalina Lupu; Cristiana Vlad; Adrian Covic
Journal:  Biomed Res Int       Date:  2020-06-10       Impact factor: 3.411

4.  Wearable health devices and personal area networks: can they improve outcomes in haemodialysis patients?

Authors:  Jeroen P Kooman; Fokko Pieter Wieringa; Maggie Han; Sheetal Chaudhuri; Frank M van der Sande; Len A Usvyat; Peter Kotanko
Journal:  Nephrol Dial Transplant       Date:  2020-03-01       Impact factor: 5.992

Review 5.  Artificial intelligence enabled applications in kidney disease.

Authors:  Sheetal Chaudhuri; Andrew Long; Hanjie Zhang; Caitlin Monaghan; John W Larkin; Peter Kotanko; Shashi Kalaskar; Jeroen P Kooman; Frank M van der Sande; Franklin W Maddux; Len A Usvyat
Journal:  Semin Dial       Date:  2020-09-13       Impact factor: 3.455

  5 in total

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