A systematic review of predictive risk models for diabetes complications based on large scale clinical studies

Lagani, Vincenzo; Koumakis, Lefteris; Chiarugi, Franco; Lakasing, Edin; Tsamardinos, Ioannis

doi:10.1016/j.jdiacomp.2012.11.003

Cited by 54 publications

(39 citation statements)

References 15 publications

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“…1 This could be used for clinical decision support, disease surveillance, and population health management to improve patient care. 2 Diabetes is one of the top priorities in medical science and health care management; and an abundance of data and information on these patients is therefore available.…”

Section: Special Sectionmentioning

confidence: 99%

Toward Big Data Analytics

Cichosz

Johansen

Hejlesen

2015

J Diabetes Sci Technol

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Diabetes is one of the top priorities in medical science and health care management, and an abundance of data and information is available on these patients. Whether data stem from statistical models or complex pattern recognition models, they may be fused into predictive models that combine patient information and prognostic outcome results. Such knowledge could be used in clinical decision support, disease surveillance, and public health management to improve patient care. Our aim was to review the literature and give an introduction to predictive models in screening for and the management of prevalent short-and long-term complications in diabetes. Predictive models have been developed for management of diabetes and its complications, and the number of publications on such models has been growing over the past decade. Often multiple logistic or a similar linear regression is used for prediction model development, possibly owing to its transparent functionality. Ultimately, for prediction models to prove useful, they must demonstrate impact, namely, their use must generate better patient outcomes. Although extensive effort has been put in to building these predictive models, there is a remarkable scarcity of impact studies.

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Section: Special Sectionmentioning

confidence: 99%

Toward Big Data Analytics

Cichosz

Johansen

Hejlesen

2015

J Diabetes Sci Technol

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“…Insulin treatment or insulin sensitizers, such as rosiglitazone and pioglitazone, have been testified to improve CNS insulin signaling to protect synapses from deleterious condition [20][21][22]. Paradoxically, the risk of dementia in T2DM individuals treated with insulin seemed to be higher than those with an adequate glycemic control [23], providing a challenge in the prevention and treatment of diabetic dementia.…”

Section: Introductionmentioning

confidence: 99%

Asiaticoside attenuates diabetes-induced cognition deficits by regulating PI3K/Akt/NF-κB pathway

Yin

Chen

et al. 2015

Behavioural Brain Research

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“…Table III summarizes available risk engines, along with adopted methodologies and datasets used for their development, as well as the specific patient target group and complications. The diabetes complications risk prediction models are usually based on survival analysis, regression equations and Markov modeling [71]. A different methodological framework, which is based on AI techniques, has been utilized in [67] toward personalized risk prediction of diabetic retinopathy development in patients with T1DM.…”

Section: Section Iiicdss For Diabetes Managementmentioning

confidence: 99%

A Review of Emerging Technologies for the Management of Diabetes Mellitus

Zarkogianni

Litsa

Mitsis

et al. 2015

IEEE Trans. Biomed. Eng.

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Objective High prevalence of diabetes mellitus (DM) along with the poor health outcomes and the escalated costs of treatment and care poses the need to focus on prevention, early detection and improved management of the disease. The aim of this paper is to present and discuss the latest accomplishments in sensors for glucose and lifestyle monitoring along with clinical decision support systems (CDSSs) facilitating self-disease management and supporting healthcare professionals in decision making. Methods A critical literature review analysis is conducted focusing on advances in: 1) sensors for physiological and lifestyle monitoring, 2) models and molecular biomarkers for predicting the onset and assessing the progress of DM, and 3) modeling and control methods for regulating glucose levels. Results Glucose and lifestyle sensing technologies are continuously evolving with current research focusing on the development of noninvasive sensors for accurate glucose monitoring. A wide range of modeling, classification, clustering, and control approaches have been deployed for the development of the CDSS for diabetes management. Sophisticated multiscale, multilevel modeling frameworks taking into account information from behavioral down to molecular level are necessary to reveal correlations and patterns indicating the onset and evolution of DM. Conclusion Integration of data originating from sensor-based systems and electronic health records combined with smart data analytics methods and powerful user centered approaches enable the shift toward preventive, predictive, personalized, and participatory diabetes care. Significance The potential of sensing and predictive modeling approaches toward improving diabetes management is highlighted and related challenges are identified.

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A systematic review of predictive risk models for diabetes complications based on large scale clinical studies

Cited by 54 publications

References 15 publications

Toward Big Data Analytics

Toward Big Data Analytics

Asiaticoside attenuates diabetes-induced cognition deficits by regulating PI3K/Akt/NF-κB pathway

A Review of Emerging Technologies for the Management of Diabetes Mellitus

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