ArticleJournal of diabetes science and technology2009
Data mining technologies for blood glucose and diabetes management.
Article in Journal of diabetes science and technology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis.Frontiers in digital health · 2025Pooled it
- Data-mining technologies for diabetes: a systematic review.Journal of diabetes science and technology · 2011Pooled it
- Dynamic circadian fluctuations of glycemia in patients with type 2 diabetes mellitus.Biological research · 2022Article
- Oscillatory pattern of glycemic control in patients with diabetes mellitus.Scientific reports · 2021Article
- Toward Detecting Infection Incidence in People With Type 1 Diabetes Using Self-Recorded Data (Part 1): A Novel Framework for a Personalized Digital Infectious Disease Detection System.Journal of medical Internet research · 2020Article
- Big data analytics for preventive medicine.Neural computing & applications · 2020Article
- A Reinforcement Learning-Based Method for Management of Type 1 Diabetes: Exploratory Study.JMIR diabetes · 2019Article
- How Knowledge Emerges From Artificial Intelligence Algorithm and Data Visualization for Diabetes Management.Journal of diabetes science and technology · 2019Article
- Big Data Technologies: New Opportunities for Diabetes Management.Journal of diabetes science and technology · 2015Review
- Designing a Human T-Lymphotropic Virus Type 1 (HTLV-I) Diagnostic Model using the Complete Blood Count.Iranian journal of basic medical sciences · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Data mining is the process of selecting, exploring, and modeling large amounts of data to discover unknown patterns or relationships useful to the data analyst. This article describes applications of data mining for the analysis of blood glucose and diabetes mellitus data. The diabetes management context is particularly well suited to a data mining approach. The availability of electronic health records and monitoring facilities, including telemedicine programs, is leading to accumulating huge data sets that are accessible to physicians, practitioners, and health care decision makers. Moreover, because diabetes is a lifelong disease, even data available for an individual patient may be massive and difficult to interpret. Finally, the capability of interpreting blood glucose readings is important not only in diabetes monitoring but also when monitoring patients in intensive care units. This article describes and illustrates work that has been carried out in our institutions in two areas in which data mining has a significant potential utility to researchers and clinical practitioners: analysis of (i) blood glucose home monitoring data of diabetes mellitus patients and (ii) blood glucose monitoring data from hospitalized intensive care unit patients.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.