Opening the Black Box: Exploring Temporal Pattern of Type 2 Diabetes Complications in Patient Clustering Using Association Rules and Hidden Variable Discovery

2019 ◽  
Author(s):  
Leila Yousefi ◽  
Stephen Swift ◽  
Mahir Arzoky ◽  
Lucia Sacchi ◽  
Luca Chiovato ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Association Rules ◽  
Temporal Pattern ◽  
Diabetes Complications ◽  
Black Box ◽  
Hidden Variable

2223-PUB: Association of Vitamin D Metabolites with Type 2 Diabetes Complications in Qatar

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 2223-PUB
Author(s):  
STEPHEN ATKIN ◽  
ALEXANDRA E. BUTLER
Keyword(s):  
Type 2 Diabetes ◽  
Vitamin D ◽  
Diabetes Complications ◽  
Vitamin D Metabolites

Prevalence and Risk Factors for Type 2 Diabetes Complications and Comorbidity in Sub-Saharan Africans

2019 ◽  
Author(s):  
Kenneth Ekoru ◽  
Ayo Doumatey ◽  
Amy R. Bentley ◽  
Guanjie Chen ◽  
Jie Zhou ◽  
...  
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Diabetes Complications ◽  
Sub Saharan

scholarly journals Sodium Intake and Incidence of Diabetes Complications in Elderly Patients with Type 2 Diabetes—Analysis of Data from the Japanese Elderly Diabetes Intervention Study (J-EDIT)

Nutrients ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 689
Author(s):  
Chika Horikawa ◽  
Rei Aida ◽  
Shiro Tanaka ◽  
Chiemi Kamada ◽  
Sachiko Tanaka ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Diabetic Retinopathy ◽  
Elderly Patients ◽  
Diabetes Complications ◽  
Vegetable Intake ◽  
Sodium Intake ◽  
Food Groups ◽  
High Sodium ◽  
Increased Risk

This study investigates the associations between sodium intake and diabetes complications in a nationwide cohort of elderly Japanese patients with type 2 diabetes aged 65–85. Data from 912 individuals regarding their dietary intake at baseline is analyzed and assessed by the Food Frequency Questionnaire based on food groups. Primary outcomes are times to diabetic retinopathy, overt nephropathy, cardiovascular disease (CVD), and all-cause mortality during six years. We find that mean sodium intake in quartiles ranges from 2.5 g to 5.9 g/day. After adjustment for confounders, no significant associations are observed between sodium intake quartiles and incidence of diabetes complications and mortality, except for a significant trend for an increased risk of diabetic retinopathy (p = 0.039). Among patients whose vegetable intake was less than the average of 268.7 g, hazard ratios (HRs) for diabetic retinopathy in patients in the second, third, and fourth quartiles of sodium intake compared with the first quartile were 0.87 (95% CI, 0.31–2.41), 2.61 (1.00–6.83), and 3.70 (1.37–10.02), respectively. Findings indicate that high sodium intake under conditions of low vegetable intake is associated with an elevated incidence of diabetic retinopathy in elderly patients with type 2 diabetes.

Pharmacological Management of Type 2 Diabetes Complications

2020 ◽  
Vol 18 (2) ◽  
pp. 101-103
Author(s):  
Michael Doumas ◽  
Konstantinos Imprialos ◽  
Konstantinos Stavropoulos ◽  
Vasilios G. Athyros
Keyword(s):  
Type 2 Diabetes ◽  
Diabetes Complications ◽  
Pharmacological Management

scholarly journals Effective Treatment Recommendations for Type 2 Diabetes Management Using Reinforcement Learning: Treatment Recommendation Model Development and Validation (Preprint)

2021 ◽  
Author(s):  
Xingzhi Sun ◽  
Yong Mong Bee ◽  
Shao Wei Lam ◽  
Zhuo Liu ◽  
Wei Zhao ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Type 2 Diabetes ◽  
Reinforcement Learning ◽  
Blood Lipids ◽  
Diabetes Complications ◽  
Data Driven ◽  
Lipid Lowering ◽  
Treatment Recommendation

BACKGROUND Type 2 diabetes mellitus (T2DM) and its related complications represent a growing economic burden for many countries and health systems. Diabetes complications can be prevented through better disease control, but there is a large gap between the recommended treatment and the treatment that patients actually receive. The treatment of T2DM can be challenging because of different comprehensive therapeutic targets and individual variability of the patients, leading to the need for precise, personalized treatment. OBJECTIVE The aim of this study was to develop treatment recommendation models for T2DM based on deep reinforcement learning. A retrospective analysis was then performed to evaluate the reliability and effectiveness of the models. METHODS The data used in our study were collected from the Singapore Health Services Diabetes Registry, encompassing 189,520 patients with T2DM, including 6,407,958 outpatient visits from 2013 to 2018. The treatment recommendation model was built based on 80% of the dataset and its effectiveness was evaluated with the remaining 20% of data. Three treatment recommendation models were developed for antiglycemic, antihypertensive, and lipid-lowering treatments by combining a knowledge-driven model and a data-driven model. The knowledge-driven model, based on clinical guidelines and expert experiences, was first applied to select the candidate medications. The data-driven model, based on deep reinforcement learning, was used to rank the candidates according to the expected clinical outcomes. To evaluate the models, short-term outcomes were compared between the model-concordant treatments and the model-nonconcordant treatments with confounder adjustment by stratification, propensity score weighting, and multivariate regression. For long-term outcomes, model-concordant rates were included as independent variables to evaluate if the combined antiglycemic, antihypertensive, and lipid-lowering treatments had a positive impact on reduction of long-term complication occurrence or death at the patient level via multivariate logistic regression. RESULTS The test data consisted of 36,993 patients for evaluating the effectiveness of the three treatment recommendation models. In 43.3% of patient visits, the antiglycemic medications recommended by the model were concordant with the actual prescriptions of the physicians. The concordant rates for antihypertensive medications and lipid-lowering medications were 51.3% and 58.9%, respectively. The evaluation results also showed that model-concordant treatments were associated with better glycemic control (odds ratio [OR] 1.73, 95% CI 1.69-1.76), blood pressure control (OR 1.26, 95% CI, 1.23-1.29), and blood lipids control (OR 1.28, 95% CI 1.22-1.35). We also found that patients with more model-concordant treatments were associated with a lower risk of diabetes complications (including 3 macrovascular and 2 microvascular complications) and death, suggesting that the models have the potential of achieving better outcomes in the long term. CONCLUSIONS Comprehensive management by combining knowledge-driven and data-driven models has good potential to help physicians improve the clinical outcomes of patients with T2DM; achieving good control on blood glucose, blood pressure, and blood lipids; and reducing the risk of diabetes complications in the long term.

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