scholarly journals Type-2 Diabetes Mellitus Diagnosis from Time Series Clinical Data Using Deep Learning Models

2018 ◽  
pp. 468-478 ◽  
Author(s):  
Zakhriya Alhassan ◽  
A. Stephen McGough ◽  
Riyad Alshammari ◽  
Tahani Daghstani ◽  
David Budgen ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Time Series ◽  
Type 2 Diabetes Mellitus ◽  
Deep Learning ◽  
Clinical Data ◽  
Learning Models ◽  
Diabetes Mellitus Diagnosis

scholarly journals METFORMIN USE IN DIABETES PRIOR TO HOSPITALIZATION: EFFECTS ON MORTALITY IN COVID-19

2020 ◽  
Vol 26 (10) ◽  
pp. 1166-1172
Author(s):  
Jinghong Li ◽  
Qi Wei ◽  
Willis X. Li ◽  
Karen C. McCowen ◽  
Wei Xiong ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Severe Acute Respiratory Syndrome ◽  
Angiotensin Converting Enzyme ◽  
Clinical Data ◽  
Laboratory Data ◽  
Converting Enzyme ◽  
Angiotensin Converting Enzyme 2

Objective: Although type 2 diabetes mellitus (T2DM) has been reported as a risk factor for coronavirus disease 2019 (COVID-19), the effect of pharmacologic agents used to treat T2DM, such as metformin, on COVID-19 outcomes remains unclear. Metformin increases the expression of angiotensin converting enzyme 2, a known receptor for severe acute respiratory syndrome coronavirus 2. Data from people with T2DM hospitalized for COVID-19 were used to test the hypothesis that metformin use is associated with improved survival in this population. Methods: Retrospective analyses were performed on de-identified clinical data from a major hospital in Wuhan, China, that included patients with T2DM hospitalized for COVID-19 during the recent epidemic. One hundred and thirty-one patients diagnosed with COVID-19 and T2DM were used in this study. The primary outcome was mortality. Demographic, clinical characteristics, laboratory data, diabetes medications, and respiratory therapy data were also included in the analysis. Results: Of these 131 patients, 37 used metformin with or without other antidiabetes medications. Among the 37 metformin-taking patients, 35 (94.6%) survived and 2 (5.4%) did not survive. The mortality rates in the metformin-taking group versus the non-metformin group were 5.4% (2/37) versus 22.3% (21/94). Using multivariate analysis, metformin was found to be an independent predictor of survival in this cohort ( P = .02). Conclusion: This study reveals a significant association between metformin use and survival in people with T2DM diagnosed with COVID-19. These clinical data are consistent with potential benefits of the use of metformin for COVID-19 patients with T2DM. Abbreviations: ACE2 = angiotensin-converting enzyme 2; AMPK = AMP-activated protein kinase; BMI = body mass index; COVID-19 = coronavirus disease 2019; SARSCoV-2 = severe acute respiratory syndrome coronavirus 2; T2DM = type 2 diabetes mellitus

scholarly journals Assessment of serum arginase I as a type 2 diabetes mellitus diagnosis biomarker in patients

2014 ◽  
Vol 8 (2) ◽  
pp. 585-590 ◽  
Author(s):  
SONG WANG ◽  
FANG FANG ◽  
WEN-BO JIN ◽  
XIA WANG ◽  
DA-WEI ZHENG
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Diabetes Mellitus Diagnosis ◽  
Arginase I

scholarly journals Delay in the Detrended Fluctuation Analysis Crossover Point as a Risk Factor for Type 2 Diabetes Mellitus

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Manuel Varela ◽  
Luis Vigil ◽  
Carmen Rodriguez ◽  
Borja Vargas ◽  
Rafael García-Carretero
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Time Series ◽  
Type 2 Diabetes Mellitus ◽  
Detrended Fluctuation Analysis ◽  
Fluctuation Analysis ◽  
Crossover Point ◽  
Increased Risk ◽  
Detrended Fluctuation

Detrended Fluctuation Analysis (DFA) measures the complexity of a glucose time series obtained by means of a Continuous Glucose Monitoring System (CGMS) and has proven to be a sensitive marker of glucoregulatory dysfunction. Furthermore, some authors have observed a crossover point in the DFA, signalling a change of dynamics, arguably dependent on the beta-insular function. We investigate whether the characteristics of this crossover point have any influence on the risk of developing type 2 diabetes mellitus (T2DM). To this end we recruited 206 patients at increased risk of T2DM (because of obesity, essential hypertension, or a first-degree relative with T2DM). A CGMS time series was obtained, from which the DFA and the crossover point were calculated. Patients were then followed up every 6 months for a mean of 17.5 months, controlling for the appearance of T2DM diagnostic criteria. The time to crossover point was a significant predictor risk of developing T2DM, even after adjusting for other variables. The angle of the crossover was not predictive by itself but became significantly protective when the model also considered the crossover point. In summary, both a delay and a blunting of the crossover point predict the development of T2DM.

scholarly journals Development of a Predictive Model for Type 2 Diabetes Mellitus Using Genetic and Clinical Data

2011 ◽  
Vol 2 (2) ◽  
pp. 75-82 ◽  
Author(s):  
Juyoung Lee ◽  
Bhumsuk Keam ◽  
Eun Jung Jang ◽  
Mi Sun Park ◽  
Ji Young Lee ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Predictive Model ◽  
Clinical Data

scholarly journals Development of a predictive risk model for all-cause mortality in patients with diabetes in Hong Kong

2021 ◽  
Vol 9 (1) ◽  
pp. e001950
Author(s):  
Sharen Lee ◽  
Jiandong Zhou ◽  
Keith Sai Kit Leung ◽  
William Ka Kei Wu ◽  
Wing Tak Wong ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Deep Learning ◽  
Risk Model ◽  
All Cause Mortality ◽  
Patients With Diabetes ◽  
Predictive Risk

IntroductionPatients with diabetes mellitus are risk of premature death. In this study, we developed a machine learning-driven predictive risk model for all-cause mortality among patients with type 2 diabetes mellitus using multiparametric approach with data from different domains.Research design and methodsThis study used territory-wide data of patients with type 2 diabetes attending public hospitals or their associated ambulatory/outpatient facilities in Hong Kong between January 1, 2009 and December 31, 2009. The primary outcome is all-cause mortality. The association of risk variables and all-cause mortality was assessed using Cox proportional hazards models. Machine and deep learning approaches were used to improve overall survival prediction and were evaluated with fivefold cross validation method.ResultsA total of 273 678 patients (mean age: 65.4±12.7 years, male: 48.2%, median follow-up: 142 (IQR=106–142) months) were included, with 91 155 deaths occurring on follow-up (33.3%; annualized mortality rate: 3.4%/year; 2.7 million patient-years). Multivariate Cox regression found the following significant predictors of all-cause mortality: age, male gender, baseline comorbidities, anemia, mean values of neutrophil-to-lymphocyte ratio, high-density lipoprotein-cholesterol, total cholesterol, triglyceride, HbA1c and fasting blood glucose (FBG), measures of variability of both HbA1c and FBG. The above parameters were incorporated into a score-based predictive risk model that had a c-statistic of 0.73 (95% CI 0.66 to 0.77), which was improved to 0.86 (0.81 to 0.90) and 0.87 (0.84 to 0.91) using random survival forests and deep survival learning models, respectively.ConclusionsA multiparametric model incorporating variables from different domains predicted all-cause mortality accurately in type 2 diabetes mellitus. The predictive and modeling capabilities of machine/deep learning survival analysis achieved more accurate predictions.

scholarly journals Análise Espaço-Temporal da Mortalidade por Diabetes Mellitus no Rio Grande do Sul (Health geography contribution to the analysis of diabetes mellitus mortality in Rio Grande do Sul)

2016 ◽  
Vol 9 (6) ◽  
pp. 1836
Author(s):  
Jéssica Mazutti Penso ◽  
Eduardo Périco
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Time Series ◽  
Type 2 Diabetes Mellitus ◽  
Rio Grande ◽  
Health Geography ◽  
Rio Grande Do Sul ◽  
Female Population ◽  
Local Moran

Compreender a relação entre o espaço geográfico e os eventos em saúde é relevante, uma vez que o ambiente influencia no processo de saúde e doença da população. Nesta perspectiva, a geografia da saúde surge como um contributo para a análise dos eventos em saúde. O presente estudo buscou utilizar a abordagem espacial, na perspectiva da geografia da saúde, para analisar o perfil epidemiológico da mortalidade por Diabetes Mellitus tipo 2, nas regiões de saúde do estado do Rio Grande do Sul, no período de 2003 a 2012. Foi realizado um estudo ecológico, observacional, de dados agregados humanos e séries temporais. Foi utilizado o Índice de Moran Global, o Índice de Moran Local, a correlação de Pearson e o teste G. A distribuição, e a análise espacial, possibilitou compreender o perfil geográfico da mortalidade pela doença nas regiões de saúde do estado. O Índice de Moran Local apontou para clusters significativo de regiões alto-alto, mas com tendência de variação destes clusters nas séries temporais em análise. Os coeficientes anuais apresentaram aumento de 59% na mortalidade por Diabetes Mellitus tipo 2 no período estudado. A população mais atingida em relação à mortalidade por Diabetes Mellitus tipo 2 foi a população do sexo feminino, de cor / raça branca e com escolaridade de 0 a 3 anos de estudo. A utilização de técnicas de análise espaço-temporal serviu como um contributo importante para a análise do perfil epidemiológico da mortalidade por Diabetes Mellitus tipo 2.   A B S T R A C T To understand the relationship between the geographic area and the health event is relevant, since the environment influences the process of health and disease populations. In this perspective, health geography appears as a contribution to the analysis of health events. This study sought to use the spatial approach, from the perspective of health geography to analyze the epidemiology of mortality by type 2 diabetes mellitus, in the health regions of Rio Grande do Sul, in the period 2003-2012. An ecological study, observational, human aggregated data and time series was performed. Global Moran Index was used, the local Moran index, Pearson's correlation and G test. The distribution and spatial analysis, enabled us to understand the geographic profile of mortality from the disease in the state health regions. The local Moran index pointed to significant clusters of high-high regions, but with a tendency of variation of these clusters in time series analysis. The annual coefficient increased by 59% in mortality from type 2 diabetes during the study period. The population most affected in relation to mortality from type 2 diabetes mellitus is the female population, color / white race and education from 0 to 3 years of study. The use of space-time analysis techniques served as an important contribution to the analysis of the epidemiology of mortality in type 2 diabetes mellitus. Keywords: Medical Geography, Geographic Information System, Moran index, Epidemiology, Collective Health.   

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