Realization of a service for the long-term risk assessment of diabetes-related complications

Lagani, Vincenzo; Chiarugi, Franco; Manousos, Dimitris; Verma, Vivek Kumar; Fursse, Joanna; Marias, Kostas; Tsamardinos, Ioannis

doi:10.1016/j.jdiacomp.2015.03.011

Cited by 24 publications

(19 citation statements)

References 21 publications

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“…It is worth noting that DM complications are far less common and severe in people with well-controlled blood glucose levels. Many of those complications have been studied through machine learning and data mining applications [78], [79], [80], [81], [82], [83], [84], [85], [87], [88], [89], [90], [92], [94], [95], [96], [97].…”

Section: Dm Through Machine Learning and Data Miningmentioning

confidence: 99%

“…In a more general aspect, Lagani et al targeted several diabetic complications, such as cardiovascular diseases (CVD), hypoglycemia, ketoacidosis, microalbuminuria, proteinuria, neuropathy, and retinopathy [78], [79]. In [78], in an effort to identify the smallest set of clinical parameters with the best predictive accuracy, involving the aforementioned diabetic complications, a set of predictive models was used that had been developed through data mining and machine learning approaches.…”

Section: Dm Through Machine Learning and Data Miningmentioning

confidence: 99%

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Machine Learning and Data Mining Methods in Diabetes Research

Kavakiotis

Tsave

Salifoglou

et al. 2017

Computational and Structural Biotechnology Journal

1,061

530

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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.

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Section: Dm Through Machine Learning and Data Miningmentioning

confidence: 99%

Section: Dm Through Machine Learning and Data Miningmentioning

confidence: 99%

Machine Learning and Data Mining Methods in Diabetes Research

Kavakiotis

Tsave

Salifoglou

et al. 2017

Computational and Structural Biotechnology Journal

1,061

530

View full text Add to dashboard Cite

show abstract

“…In addition, many complications of DM have been studied using machine learning approaches and data mining techniques. In a more general aspect, Lagani et al (2015[ 43 ]) targeted several diabetic complications, such as cardiovascular diseases (CVD), hypoglycemia, proteinuria, ketoacidosis, microalbuminuria, neuropathy, and retinopathy in an effort to identify the smallest set of clinical parameters affording the best predictive accuracy that makes use of the aforementioned diabetic complications as parameters.…”

Section: Utilization Of Bioinformatics In Diabetes Researchmentioning

confidence: 99%

Proteomic and bioinformatic discovery of biomarkers for diabetic nephropathy

Thippakorn

Schaduangrat

Nantasenamat

2018

EXCLI Journal; 17:Doc312; ISSN 1611-2156

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“…1 The incidence of diabetes mellitus (DM) is growing rapidly around the world, and DM has become the third serious chronic disease threatening human health after cancer and cardiovascular disease. 2 Type 2 diabetes mellitus (T2DM) accounts for 90%e95% of DM. T2DM erectile dysfunction (T2DMED) is one of the common complications of T2DM.…”

Section: Introductionmentioning

confidence: 99%

Effect of Icariside II and Metformin on Penile Erectile Function, Histological Structure, Mitochondrial Autophagy, Glucose-Lipid Metabolism, Angiotensin II and Sex Hormone in Type 2 Diabetic Rats With Erectile Dysfunction

Zhang

et al. 2020

Sexual Medicine

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Introduction: Type 2 diabetes mellitus erectile dysfunction (T2DMED) is one of the common complications of type 2 diabetes mellitus (T2DM). Icariside II (ICA II), a flavonoid derived from Epimedium, has been shown to improve erectile function in T2DMED rats. Aim: To investigate the effect of ICA II and metformin (MET) on penile erectile function, mitochondrial autophagy, glucose-lipid metabolism in rats with T2DMED. Methods: In the control and T2DMED groups, rats were administered normal saline. In the MET group, rats were administered MET for 0.2 g/kg/day. In the ICA IIþMET group, rats were administered ICA II for 10 mg/kg/day and MET for 0.2 g/kg/day. Results: The number of mating rats, number of erectile rats, erection rate, erection frequency, intracorneal pressure, and intracorneal pressure/mean arterial pressure in the ICA IIþMET group and control group were significantly higher than corresponding values in than T2DMED group. The absolute values of fasting plasma glucose, glycated haemoglobin in the ICA IIþMET group, MET group, and control group were significantly lower than in the T2DMED group. The advanced glycation end product (AGE) values in the ICA IIþMET group and the MET group were lower than in the T2DMED group. The receptors for the AGE values and angiotensin II values in the ICA IIþMET group were lower than in the T2DMED and MET groups. The highdensity lipoprotein values, testosterone values, nitric oxide synthase activity, and cyclic guanosine monophosphate content in the ICA IIþMET and control groups were higher than in the T2DMED group. The lowdensity lipoprotein values, triglyceride values, estradiol values, and total cholesterol values in the ICA IIþMET and control groups were lower than in the T2DMED group. Conclusion: ICA II could increase erectile function and smooth muscle cell/collagen fibril proportions, decreased mitochondrial autophagy, and AGE concentrations and improve lipid metabolism, nitric oxide synthase activity, cyclic guanosine monophosphate content, testosterone, estradiol, and Ang II in rat with T2DMED. Zhang J, Li S, Zhang S, et al.

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Realization of a service for the long-term risk assessment of diabetes-related complications

Cited by 24 publications

References 21 publications

Machine Learning and Data Mining Methods in Diabetes Research

Machine Learning and Data Mining Methods in Diabetes Research

Proteomic and bioinformatic discovery of biomarkers for diabetic nephropathy

Effect of Icariside II and Metformin on Penile Erectile Function, Histological Structure, Mitochondrial Autophagy, Glucose-Lipid Metabolism, Angiotensin II and Sex Hormone in Type 2 Diabetic Rats With Erectile Dysfunction

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