Skip to main content
We gratefully acknowledge support from the Simons Foundation, member institutions, and all contributors. Donate
arXiv logo
Cornell University Logo

Computer Science > Machine Learning

arXiv:2010.03948 (cs)
[Submitted on 6 Oct 2020]

Title:Artificial intelligence supported anemia control system (AISACS) to prevent anemia in maintenance hemodialysis patients

Authors:Toshiaki Ohara, Hiroshi Ikeda, Yoshiki Sugitani, Hiroshi Suito, Viet Quang Huy Huynh, Masaru Kinomura, Soichiro Haraguchi, Kazufumi Sakurama
View PDF
Abstract:Anemia, for which erythropoiesis-stimulating agents (ESAs) and iron supplements (ISs) are used as preventive measures, presents important difficulties for hemodialysis patients. Nevertheless, the number of physicians able to manage such medications appropriately is not keeping pace with the rapid increase of hemodialysis patients. Moreover, the high cost of ESAs imposes heavy burdens on medical insurance systems. An artificial-intelligence-supported anemia control system (AISACS) trained using administration direction data from experienced physicians has been developed by the authors. For the system, appropriate data selection and rectification techniques play important roles. Decision making related to ESAs poses a multi-class classification problem for which a two-step classification technique is introduced. Several validations have demonstrated that AISACS exhibits high performance with correct classification rates of 72-87% and clinically appropriate classification rates of 92-98%.
Comments: 14 pages and 7 figures
Subjects: Machine Learning (cs.LG)
ACM classes: H.4.2; I.2.1; I.5.4; J.3
Cite as: arXiv:2010.03948 [cs.LG]
  (or arXiv:2010.03948v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2010.03948

Submission history

From: Yoshiki Sugitani [view email]
[v1] Tue, 6 Oct 2020 03:59:37 UTC (2,016 KB)
Full-text links:

Access Paper:

  • View PDF
  • Other Formats
view license
Current browse context:
cs.LG
< prev   |   next >
new | recent | 2020-10
Change to browse by:
cs

References & Citations

DBLP - CS Bibliography

listing | bibtex
export BibTeX citation

Bookmark

BibSonomy logo Reddit logo

Bibliographic and Citation Tools

Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)

Code, Data and Media Associated with this Article

alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
Papers with Code (What is Papers with Code?)
ScienceCast (What is ScienceCast?)

Demos

Replicate (What is Replicate?)
Hugging Face Spaces (What is Spaces?)
TXYZ.AI (What is TXYZ.AI?)

Recommenders and Search Tools

Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)
IArxiv Recommender (What is IArxiv?)

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)