SynthesisJournal of diabetes science and technology2011
Data-mining technologies for diabetes: a systematic review.
Synthesis in Journal of diabetes science and technology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Automated data extraction of electronic medical records: Validity of data mining to construct research databases for eligibility in gastroenterological clinical trials.Upsala journal of medical sciences · 2022Article
- A Smartphone-Based Decision Support Tool for Predicting Patients at Risk of Chemotherapy-Induced Nausea and Vomiting: Retrospective Study on App Development Using Decision Tree Induction.JMIR mHealth and uHealth · 2021Article
- A survey on data mining techniques used in medicine.Journal of diabetes and metabolic disorders · 2021Article
- Understanding the Effect of Energy Density and Formulation Factors on the Printability and Characteristics of SLS Irbesartan Tablets-Application of the Decision Tree Model.Pharmaceutics · 2021Article
- Missing data techniques in classification for cardiovascular dysautonomias diagnosis.Medical & biological engineering & computing · 2020Article
- Effectiveness of Social Cognitive Theory-Based Interventions for Glycemic Control in Adults With Type 2 Diabetes Mellitus: Protocol for a Systematic Review and Meta-Analysis.JMIR research protocols · 2020Article
- Identification of Potential Type II Diabetes in a Large-Scale Chinese Population Using a Systematic Machine Learning Framework.Journal of diabetes research · 2020Article
- Accurate and rapid screening model for potential diabetes mellitus.BMC medical informatics and decision making · 2019Article
- Semi supervised data mining model for the prognosis of pre-diabetic conditions in type 2 Diabetes Mellitus.Bioinformation · 2019Article
- Identification of Potential Type II Diabetes in a Chinese Population with a Sensitive Decision Tree Approach.Journal of diabetes research · 2019Article
- A machine learning-based framework to identify type 2 diabetes through electronic health records.International journal of medical informatics · 2017Article
- Data Mining of a Remote Behavioral Tracking System for Type 2 Diabetes Patients: A Prospective Cohort Study.JMIR diabetes · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe objective of this study is to conduct a systematic review of applications of data-mining techniques in the field of diabetes research.
methodWe searched the MEDLINE database through PubMed. We initially identified 31 articles by the search, and selected 17 articles representing various data-mining methods used for diabetes research. Our main interest was to identify research goals, diabetes types, data sets, data-mining methods, data-mining software and technologies, and outcomes.
resultsThe applications of data-mining techniques in the selected articles were useful for extracting valuable knowledge and generating new hypothesis for further scientific research/experimentation and improving health care for diabetes patients. The results could be used for both scientific research and real-life practice to improve the quality of health care diabetes patients.
conclusionsData mining has played an important role in diabetes research. Data mining would be a valuable asset for diabetes researchers because it can unearth hidden knowledge from a huge amount of diabetes-related data. We believe that data mining can significantly help diabetes research and ultimately improve the quality of health care for diabetes patients.
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What OpenQuestion holds
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.