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Cited by 26Data Stream Mining TechniquesArtificial Intelligence in HealthcareMachine Learning in Healthcare

Evaluation of Stream Mining Classifiers for Real-Time Clinical Decision Support System: A Case Study of Blood Glucose Prediction in Diabetes Therapy

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Fong, Simon, Zhang, Yang, Fiaidhi, Jinan, Mohammed, Osama, Mohammed, Sabah·2013·BioMed Research International
DOI 10.1155/2013/274193PMC PMC3791638
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Abstract

Earlier on, a conceptual design on the real-time clinical decision support system (rt-CDSS) with data stream mining was proposed and published. The new system is introduced that can analyze medical data streams and can make real-time prediction. This system is based on a stream mining algorithm called VFDT. The VFDT is extended with the capability of using pointers to allow the decision tree to remember the mapping relationship between leaf nodes and the history records. In this paper, which is a sequel to the rt-CDSS design, several popular machine learning algorithms are investigated for their suitability to be a candidate in the implementation of classifier at the rt-CDSS. A classifier essentially needs to accurately map the events inputted to the system into one of the several predefined classes of assessments, such that the rt-CDSS can follow up with the prescribed remedies being recommended to the clinicians. For a real-time system like rt-CDSS, the major technological challenges lie in the capability of the classifier to process, analyze and classify the dynamic input data, quickly and upmost reliably. An experimental comparison is conducted. This paper contributes to the insight of choosing and embedding a stream mining classifier into rt-CDSS with a case study of diabetes therapy.

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  • With the inputs of SMBG and non-rapid insulin, Fong et al.'s work is worth discussing today because SMBG and non-rapid insulin formulations are still used in many parts of the world.

    “With the inputs of SMBG and non-rapid insulin, this work is worth discussing today because SMBG and non-rapid insulin formulations are still used in many parts of the world.”

    From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as [82]

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Research works printed but not linked: 24

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Cited by

Sorted by citations

Authors
Fong, S., Zhang, Y., Fiaidhi, J., Mohammed, O., & Mohammed, S.
Affiliations
  • University of Macau (MO)
  • Lakehead University (CA)
Corresponding author
Fong, Simon
ORCID
Venue
BioMed Research International
Publisher
Wiley
Published
2013
Volume
2013
Pages
1-16
ISSN
  • 1110-7243 (ISSN-L)
  • 2314-6133
  • 2314-6141
DOI
10.1155/2013/274193
PMC
PMC3791638
OpenAlex
W2163909061
Cited by
26
References
0
Citations by year
2026: 1 · 2025: 2 · 2024: 1 · 2023: 1 · 2022: 1 · 2021: 3 · 2020: 4 · 2019: 2 · 2018: 3 · 2017: 3 · 2016: 3 · 2015: 2
FWCI
1.84
Type
journal article
Language
en
Open access
Licence
Locations
  • BioMed Research International (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
University of Macau (MYRG073(Y2-L2)-FST12-FCC)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Data Stream Mining Techniques
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords
data stream mining, blood glucose prediction, diabetes treatment, classifier comparison, decision tree classification, clinical decision support
MeSH terms
Algorithms, Bayes Theorem, Blood Glucose, Computer Systems, Diabetes Mellitus, Humans, ROC Curve, Area Under Curve, Decision Support Systems, Clinical

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