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Using data mining techniques to predict hospitalization of hemodialysis patients
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“A similar example of using predictive models in healthcare are: blood glucose level prediction [23], mortality prediction by clustering electronic health data [40], and a predictive decision making system for dialysis patients [41].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [41]
In the citing paper
“Nowadays, upon dealing with complex and noisy data, the C4.5 algorithm is used which is estimating the error rate of initial nodes and pruning the tree to make a more efficient sub-tree [41,51].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [41]
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Metadata
- Authors
- Yeh, J.-Y., Wu, T.-H., & Tsao, C.-W.
- Affiliations
- National Chiayi University (TW)
- National Taipei University (TW)
- Corresponding author
- Yeh, Jinn-Yi
- Venue
- Decision Support Systems
- Publisher
- Elsevier BV
- Published
- 2011-01
- Volume
- 50
- Issue
- 2
- Pages
- 439-448
- ISSN
- 0167-9236 (ISSN-L)
- OpenAlex
- W2060695172
- Cited by
- 110
- References
- 30
- Citations by year
- 2025: 5 · 2024: 3 · 2023: 4 · 2022: 6 · 2021: 8 · 2020: 8 · 2019: 8 · 2018: 12 · 2017: 12 · 2016: 9 · 2015: 11 · 2014: 8 · 2013: 6 · 2012: 7
- FWCI
- 9.58
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Decision Support Systems (publishedVersion)
- Funders
- National Science Council
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Nephrology › Dialysis and Renal Disease Management
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Keywords
- hemodialysis, hospitalization prediction, temporal abstraction, temporal patterns, biochemical data, decision support system, clinical decision support
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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