Innovations in personalised diabetes care and risk management

Nijpels, Giel; Beulens, Joline W.J.; Heijden, Amber A. van der; Elders, Petra

doi:10.1177/2047487319880043

Cited by 14 publications

(12 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the same way, PC 3 is also related to proteins, which is somehow different from PC 2 . Cholesterol (CHOL) and triglycerides (TG) have more loads on PC 4 that may be because diabetes is often accompanied by diseases such as high blood pressure and hyperlipidemia (Nijpels et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

Policyholder cluster divergence based differential premium in diabetes insurance

Tang

Qin

et al. 2021

Manage Decis Econ

View full text Add to dashboard Cite

Traditional health insurance pricing, which is based on experience rates, cannot correctly estimate the risk types of policyholders, can lead to serious adverse selection. Due to massive data volumes and developments in data analysis technology, the underwriting process can more accurately reflect the insured's risk type. Therefore, this paper based on policyholder cluster divergence proposes a differential premium approach by employing fuzzy c‐means algorithm (FCM) with an extended initial multistate Markov model to formulate the differential premium that matches the policyholder's risk category. Our results confirm that the proposed differential premium approach better reveals the policyholder's risk type as compared with unified pricing and effectively counteracts adverse selection.

show abstract

Section: Methodsmentioning

confidence: 99%

Policyholder cluster divergence based differential premium in diabetes insurance

Tang

Qin

et al. 2021

Manage Decis Econ

View full text Add to dashboard Cite

show abstract

“…The hyperglycemia and hyperlipidemia conditions advanced complicated diseases such as neuropathy, retinopathy, cognitive defects, Parkinson’s, cardiomyopathy, atherosclerosis, and nephropathy. Type 2 DM is an evolving disease from metabolic disorders to inflammatory complications and further contributes to the development of neuropathic pain and neuropathy, as explained in Figure 1 ( Kuniss, Freyer, Muller, Kielstein, & Muller, 2019 ; Nijpels, Beulens, van der Heijden, & Elders, 2019 ). The long-term innate immune system activation in type 2 DM is due to chronic inflammation, according to recent reports (L. Li et al, 2016 ; Muggeo, 1998 ).…”

Section: Introductionmentioning

confidence: 99%

New Advances on Pathophysiology of Diabetes Neuropathy and Pain Management: Potential Role of Melatonin and DPP-4 Inhibitors

et al. 2022

View full text Add to dashboard Cite

Pre-diabetes and diabetes are growing threats to the modern world. Diabetes mellitus (DM) is associated with comorbidities such as hypertension (83.40%), obesity (90.49%), and dyslipidemia (93.43%), creating a substantial burden on patients and society. Reductive and oxidative (Redox) stress level imbalance and inflammation play an important role in DM progression. Various therapeutics have been investigated to treat these neuronal complications. Melatonin and dipeptidyl peptidase IV inhibitors (DPP-4i) are known to possess powerful antioxidant and anti-inflammatory properties and have garnered significant attention in the recent years. In this present review article, we have reviewed the recently published reports on the therapeutic efficiency of melatonin and DPP-4i in the treatment of DM. We summarized the efficacy of melatonin and DPP-4i in DM and associated complications of diabetic neuropathy (DNP) and neuropathic pain. Furthermore, we discussed the mechanisms of action and their efficacy in the alleviation of oxidative stress in DM.

show abstract

“…Moreover, disease-related complications adversely affect the quality-of-life and treatment for diabetic patients with complications are costlier than for those without complications [2, 3]. Therefore, there is a need for better risk-stratified care, which would enable early complication detection and prevention [4, 5]. Most diabetes-related complications take several years to develop [6].…”

Section: Introductionmentioning

confidence: 99%

Identifying Sequential Complication and Mortality Patterns in Diabetes Mellitus: Comparisons of Machine Learning Methodologies

Zhou

Lee²,

Wong

et al. 2020

Preprint

View full text Add to dashboard Cite

BackgroundDiabetes mellitus-related complications adversely affect the quality of life. Better risk-stratified care through mining of sequential complication patterns is needed to enable early detection and prevention.MethodsUnivariable and multivariate logistic regression was used to identify significant variables that can predict mortality. A sequence analysis method termed Prefixspan was applied to identify the most common couple, triple, quadruple, quintuple and sextuple sequential complication patterns in the directed comorbidity pathology network. A knowledge enhanced CPT+ (KCPT+) sequence prediction model is developed to predict the next possible outcome along the progression trajectories of diabetes-related complications.FindingsA total of 14,144 diabetic patients (51% males) were included. Acute myocardial infarction (AMI) without known ischaemic heart disease (IHD) (odds ratio [OR]: 2.8, 95% CI: [2.3, 3.4]), peripheral vascular disease (OR: 2.3, 95% CI: [1.9, 2.8]), dementia (OR: 2.1, 95% CI: [1.8, 2.4]), and IHD with AMI (OR: 2.4, 95% CI: [2.1, 2.6]) are the most important multivariate predictors of mortality. KCPT+ shows high accuracy in predicting mortality (F1 score 0.90, ACU 0.88), osteoporosis (F1 score 0.86, AUC 0.82), ophthalmological complications (F1 score 0.82, AUC 0.82), IHD with AMI (F1 score 0.81, AUC 0.85) and neurological complications (F1 score 0.81, AUC 0.83) with a particular prior complication sequence.InterpretationSequence analysis identifies the most common pattern characteristics of disease-related complications efficiently. The proposed sequence prediction model is accurate and enables clinicians to diagnose the next complication earlier, provide better risk-stratified care, and devise efficient treatment strategies for diabetes mellitus patients.

show abstract

Innovations in personalised diabetes care and risk management

Cited by 14 publications

References 55 publications

Policyholder cluster divergence based differential premium in diabetes insurance

Policyholder cluster divergence based differential premium in diabetes insurance

New Advances on Pathophysiology of Diabetes Neuropathy and Pain Management: Potential Role of Melatonin and DPP-4 Inhibitors

Identifying Sequential Complication and Mortality Patterns in Diabetes Mellitus: Comparisons of Machine Learning Methodologies

Contact Info

Product

Resources

About