Evidence map›Paper›PMID 28138367›Full record

ReviewComputational and structural biotechnology journal2017

Machine Learning and Data Mining Methods in Diabetes Research.

Ioannis Kavakiotis, Olga Tsave, Athanasios Salifoglou, Nicos Maglaveras, Ioannis Vlahavas, Ioanna Chouvarda

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Computational and structural biotechnology journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 270 papers, 16 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
270citing papers in PubMed, 16 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

270 citing papers in PubMed, 16 syntheses or guidelines pooled it.

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210 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Ioannis KavakiotisDepartment of Informatics, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece; Institute of Applied Biosciences, CERTH, Thessaloniki, Greece.
Olga TsaveLaboratory of Inorganic Chemistry, Department of Chemical Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece.
Athanasios SalifoglouLaboratory of Inorganic Chemistry, Department of Chemical Engineering, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece.
Nicos MaglaverasInstitute of Applied Biosciences, CERTH, Thessaloniki, Greece; Lab of Computing and Medical Informatics, Medical School, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece.
Ioannis VlahavasDepartment of Informatics, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece.
Ioanna ChouvardaInstitute of Applied Biosciences, CERTH, Thessaloniki, Greece; Lab of Computing and Medical Informatics, Medical School, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM.

Indexed as

Biomarker(s) identificationData miningDiabetes mellitusDiabetic complicationsDisease prediction modelsMachine learning

Identifiers

PMID28138367
PMCPMC5257026

What OpenQuestion holds

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Registered trials

None linked

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.