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Blood Glucose Dataset

Proteomexchange Dataset Pxd005677

Proteomexchange Dataset Pxd005677

RepositorySupport: Unsupported dataset by repository Title: A glucagon variant, glucagon 1-61, identified in man, controls blood glucose through regulation of insulin secretion and hepatic glucose production Description: Glucagon is secreted from pancreatic -cells, and hypersecretion (hyperglucagonemia) contributes to diabetic hyperglycemia. Molecular heterogeneity in hyperglucagonemia is poorly investigated. By screening human plasma by high-resolution-proteomics, we identified several glucagon variants among which proglucagon 1-61 (PG 1-61) appears to be the most abundant form. PG 1-61 was secreted in obese subjects before and as well after gastric bypass surgery with protein and fat as the main drivers for secretion before surgery, but glucose after. Studies in hepatocytes and in -cells demonstrated that PG 1-61 dose-dependently increased levels of cAMP, through the glucagon receptor, and increased insulin secretion and protein levels of enzymes regulating glycogenolysis and gluconeogenesis. As a consequence, PG 1-61 increased blood glucose and plasma insulin and decreased plasma levels of amino acids in vivo. Glucagon variants, such as PG 1-61, may contribute to glucose regulation by stimulating hepatic glucose production and insulin secretion. SpeciesList: scientific name: Homo sapiens (Human); NCBI TaxID: 9606; ModificationList: No PTMs are included in the dataset Continue reading >>

Diabetes Dataset Diabetes Heplots

Diabetes Dataset Diabetes Heplots

Reaven and Miller (1979) examined the relationship among blood chemistry measures ofglucose tolerance and insulin in 145 nonobese adults.They used the PRIM9 system at the Stanford Linear Accelerator Center tovisualize the data in 3D, and discovered a peculiar pattern that looked like a largeblob with two wings in different directions. After further analysis,the subjects were classified as subclinical (chemical) diabetics, overt diabetics andnormals. This study was influential in defining the stages of development of Type 2 diabetes.Overt diabetes is the most advanced stage, characterized by elevatedfasting blood glucose concentration and classical symptoms.Preceding overt diabetesis the latent or chemical diabetic stage, with no symptoms of diabetesbut demonstrable abnormality of oral or intravenous glucose tolerance. A data frame with 145 observations on the following 6 variables. relative weight, expressed as the ratio of actual weight to expected weight, given the person's height, a numeric vector diagnostic group, a factor with levels Normal Chemical_Diabetic Overt_Diabetic glutest was defined as the "area under the plasma glucose curve for the three houroral glucose tolerance test." Reaven & Miller refer to this variable as "Glucose area". instest was defined as the "area under the plasma insulin curve", and is referredto in the paper as "Insulin area". This study was influential in defining the stages of development of Type 2 diabetes.Overt diabetes is the most advanced stage, characterized by elevatedfasting blood glucose concentration and classical symptoms.Preceding overt diabetesis the latent or chemical diabetic stage, with no symptoms of diabetesbut demonstrable abnormality of oral or intravenous glucose tolerance. Andrews, D. F. & Herzberg, A. M. (1985). D Continue reading >>

The Dhs Program User Forum: Dataset Use In Stata Analyzing Blood Pressure And Blood Glucose

The Dhs Program User Forum: Dataset Use In Stata Analyzing Blood Pressure And Blood Glucose

I am trying to reconcile how to identify the appropriate sample to analyze for the blood pressure and blood glucose sample (which is half of the households selected for the male survey, see section 1.6.1 and 1.6.4 in final DHS report) in the 2013 Namibia DHS. Table 17.1 (pg 237 in the DHS report) shows that 2,584 women and 2,163 men age 35-64 were eligible for these tests. Among these individuals, 80.7% of women and 70.7% of men had their blood pressure measured, and 75% of women and 63.8% of men had their blood glucose measured. This would equal the following sample sizes which I can more or less match exactly in the datasets I downloaded based on identifying the eligible participants who consented and had no reported issues with the blood sample. Blood Pressure 2093 1536 Blood Glucose 1938 1384 Our question is why are the following numbers reported in measured blood pressure tables "17.4.1 Blood pressure status: Women" "17.4.2 Blood pressure status: Men" and "17.7.1 Prevalence of diabetes by background characteristics: Women" and "17.7.2 Prevalence of diabetes by background characteristics: Men". Blood Pressure 2048 1406 Blood Glucose 1873 1,221 It is not clear how using the dataset to identify eligible males and females in households selected for the male interview and consented and had biologically plausible values changes from the sample listed in Table 17.1 to the sample size in the final prevalence tables: 17.4.1 and 17.4.2 for blood pressure and 17.7.1 and 17.7.2 for blood glucose. Would someone be able to help me reconcile this? I reviewed the information you provided and the data in the datasets, and I'm giving some summaries based on the Stata data below: 1) The sample is all men and women age 35-64 in the half of households that were selected for the men's Continue reading >>

Department Of Health | 1.1 The Dataset

Department Of Health | 1.1 The Dataset

The NDOQRIN diabetes dataset has considerable compatibility with similar international datasets including the DiabCare dataset. The NDOQRIN dataset was enhanced and used as the basis of this national initiative, aimed at improving diabetes care through a structured approach to patient management. This was achieved by linking the minimum dataset to the NSW Clinical Management Guidelines for Diabetes 12 , thence enhanced over the years. This minimum dataset is suitable for use in primary care [where it is known as the Recommended GP Subset of the NDOQRIN Dataset], Specialist practice and Diabetes Centre settings. It has been developed in a scannable format (see below [The Software]) as a single page with required written data kept to a minimum, most fields being yes/no or other choice options. For the 2009 collection, after suggestions from the Diabetes Centres and specialists who have participated previously were considered, as well as experience from the 2006 Diabetes Collaborative Project, six new data elements were added to the survey data collection form and several were removed from the 2006 form. The 2009 dataset was used in 2011 with the addition of just two new fields related to eGFR [see Whats New? (E) above for more detail]: The separate Paediatric/Adolescent form designed specifically for patients with diabetes who were aged eighteen years or less, was not used (since there were no Paediatric Sites participants and thus no Paediatric data collected in 2011). The scannable form is one page in length. Definitions for each data field, including all valid field types, were printed on the reverse side of the forms, [Appendix 1 Copy of Forms]. The data dictionary [indicating field type, size and transfer protocol requirements], was updated with the above changes an Continue reading >>

Selected Data-sets From Publications By Martin Bland

Selected Data-sets From Publications By Martin Bland

Selected Data-sets from Publications by Martin Bland If you use any of these data sets in publications or teaching material, pleaseacknowledge both the author(s) of the paper and the original suppliers of thedata where given. Data-sets are in Stata dictionary format. They are plain text ASCII files.There is a list of variables and then the data in a rectangular array. You mayneed to edit out the dictionary, but with little modification they should bereadable by any statistical program. The missing data code is a dot, ".". Choose the publication and then the data-set from it. Use the Filebutton on your browser to copy the file to your own computer. Then use theBack button to return to this page. Data are available from the following publications. More data-sets will beadded soon. Data from Bland M. (2000) An Introduction to Medical Statistics ,Oxford University Press. Choose the data-set you want. Use the File button on your browser tocopy the file to your own computer. Then use the Back button to returnto this page. Forced expiratory volume (FEV) measurements from 57 male medical students (data from Paul Richardson). Serum triglyceride from cord blood of 282 babies (data from Tessi Hanid). Vital capacity , peak flow, and height for 44 female and 58 male medical students (data from Paul Richardson). Geriatric admissions in Wandsworth and mean peak daily temperatures for each week from May to September of 1982 and 1983 (data from Peter Fish). Height in a sample of 1794 pregnant women (data from Janet Peacock). Vitamin D in a sample of 26 healthy men (data from Douglas Maxwell). Random blood glucose in a sample of 40 medical students. Biceps skinfold thickness in a sample of Crohn's disease and coeliac disease patients (data from Douglas Maxwell). Zidovudine levels in the Continue reading >>

Machine Learning Workflow On Diabetes Data: Part01

Machine Learning Workflow On Diabetes Data: Part01

Machine Learning Workflow on Diabetes Data: Part01 Image credit Machine learning in a medical setting can help enhance medical diagnosis dramatically. This article will portray how data related to diabetes can be leveraged to predict if a person has diabetes or not. More specifically, this article will focus on how machine learning can be utilized to predict diseases such as diabetes. By the end of this article series you will be able to understand concepts like data exploration, data cleansing, feature selection, model selection, model evaluation and apply them in a practical way. Diabetes is a disease which occurs when the blood glucose level becomes high, which ultimately leads to other health problems such as heart diseases, kidney disease etc. Diabetes is caused mainly due to the consumption of highly processed food, bad consumption habits etc. According to WHO , the number of people with diabetes has been increased over the years. Anaconda (Scikit Learn, Numpy, Pandas, Matplotlib, Seaborn) Basic understanding of supervised machine learning methods: specifically classification. As a Data Scientist the most tedious task which we encounter is the acquiring and the preparation of a data set. Even though there is an abundance of data in this era, it is still hard to find a suitable data set which suits the problem you are trying to tackle. If there arent any suitable data sets to be found, you might have to create your own. In this tutorial we arent going to create our own data set, instead we will be using an existing data set called the Pima Indians Diabetes Database provided by the UCI Machine Learning Repository (famous repository for machine learning data sets). We will be performing the machine learning workflow with the Diabetes Data set provided above. When en Continue reading >>

A Benchmark Data Set For Model-based Glycemic Control In Critical Care

A Benchmark Data Set For Model-based Glycemic Control In Critical Care

A Benchmark Data Set for Model-Based Glycemic Control in Critical Care J. Geoffrey Chase , Ph.D., M.S., B.S.,1 Aaron LeCompte , B.E. (Hons),1 Geoffrey M. Shaw , MBChB, FANCZA, FJFICM,2 Amy Blakemore , B.E. (Hons),1 Jason Wong , B.E. (Hons),1 Jessica Lin , B.E. Ph.D., B.E. (Hons),3 and Christopher E. Hann , Ph.D., B.Sc.1 1University of Canterbury, Centre for Bio-Engineering, Department of Mechanical Engineering, Christchurch, New Zealand 2Department of Intensive Care, Christchurch Hospital, University of Otago School of Medicine, Christchurch, New Zealand 3University of Otago School of Medicine, Christchurch, New Zealand Correspondence to: J. Geoffrey Chase, Ph.D., M.S., B.S., University of Canterbury, Centre for Bio-Engineering, Department of Mechanical Engineering, Private Bag 4800, Christchurch, New Zealand; email address [email protected] Copyright 2008 Diabetes Technology Society This article has been cited by other articles in PMC. Hyperglycemia is prevalent in critical care. That tight control saves lives is becoming more clear, but the how and for whom in repeating the initial results remain elusive. Model-based methods can provide tight, patient-specific control, as well as providing significant insight into the etiology and evolution of this condition. However, it is still often difficult to compare results due to lack of a common benchmark. This article puts forward a benchmark data set for critical care glycemic control in a medical intensive care unit (ICU). Based on clinical patient data from SPecialized Relative Insulin and Nutrition Tables (SPRINT) studies, it provides a benchmark for comparing and analyzing performance in model-based glycemic control. Data from 20 of the first 150 postpilot patients treated under SPRINT are presented. All pat Continue reading >>

Diabetes Mellitus Medication Therapy Management Data Set | Medication Therapy Management: A Comprehensive Approach | Accesspharmacy | Mcgraw-hill Medical

Diabetes Mellitus Medication Therapy Management Data Set | Medication Therapy Management: A Comprehensive Approach | Accesspharmacy | Mcgraw-hill Medical

Intensive glycemic control can reduce microvascular complications of diabetes. The hemoglobin A1c goal for most patients is less than 7%; however, goals for glycemic control should be individualized based on presence of concurrent illness or complications, risk of hypoglycemia, and life expectancy. To reduce macrovascular complications, management of cardiac risk factors such as hyperten-sion and hyperlipidemia is necessary; glycemic control alone is unlikely to prevent cardiovascular morbidity and mortality. MTM providers should work with patients and the healthcare team to tailor medication regimens that achieve therapeutic goals, promote adherence, reduce the risk of complications, and maximize quality of life for patients with diabetes. Diabetes mellitus (DM) is a group of disorders characterized by hyperglycemia due to insulin resistance, reduced insulin secretion, or both. Diabetes may result in chronic complications including microvascular, macrovascular, and neuropathic disorders. 1 Diabetes currently affects 25.8 million people in the United States, representing approximately 8.3% of the population. 2 Prediabetes, defined as impaired fasting glucose or impaired glucose tolerance, affects about 35% of adults over the age of 20, approximately 79 million people. 2 Diabetes is a large economic burden in the United Statestotal costs are estimated at 69 billion in indirect medical costs due to reduced productivity. 3 The most common forms of diabetes include type 1, type 2, and gestational diabetes. Type 2 diabetes mellitus (T2DM) is the most prevalent, accounting for 95% of cases of diabetes diagnosed in adults. Approximately 5% of those diagnosed with diabetes have type 1 diabetes mellitus (T1DM). It appears most often in children and young adults, although it may Continue reading >>

Solved: Dataset #2 Contains The Glucose Blood Level (mg/10... | Chegg.com

Solved: Dataset #2 Contains The Glucose Blood Level (mg/10... | Chegg.com

home / study / math / statistics and probability / statistics and probability questions and answers / Dataset #2 Contains The Glucose Blood Level (mg/100ml) After A 12 Hour Fast For A Random Sample ... Question: Dataset #2 contains the glucose blood level (mg/100ml) after a12 hour fast for a random sample o... Dataset #2 contains the glucose blood level (mg/100ml) after a12 hour fast for a random sample of 70 women. Reference: AmericanJ. Clin. Nutr. Vol. 19, 345-351. b. Produce a stem and leaf diagram for the dataset. d. Produce a normal probability plot of the dataset. 2) Initial Analysis ofthe Dataset. Based on the above: a. Identify and list any outliersin the dataset. b. Assess the normality of thedataset and justify your assessment. 3) Descriptive Analysiswithout outliers. Remove any outliers identified by your aboveanalysis, and, using your revised dataset with the outliersremoved: a. Produce a histogram of therevised dataset. b. Produce a stem and leafdiagram for the revised dataset. c. Produce a boxplot of therevised dataset. d. Produce a probability plot ofthe revised dataset. 4) Analysis of theDataset without outliers. Based on the descriptive analysisconducted above: b. Assess the normality of therevised dataset. Comment on any changes in yourassessment of normality. Continue reading >>

Personalized Glucose Forecasting For Type 2 Diabetes Using Data Assimilation

Personalized Glucose Forecasting For Type 2 Diabetes Using Data Assimilation

Personalized glucose forecasting for type 2 diabetes using data assimilation Affiliation: Department of Biomedical Informatics, Columbia University, New York, New York, United States of America Affiliation: Department of Biomedical Informatics, Columbia University, New York, New York, United States of America Affiliation: Departments of Engineering Sciences and Mechanics, Neurosurgery, and Biomedical Engineering, Pennsylvania State University, University Park, Pennsylvania, United States of America Affiliation: Department of Medicine, Columbia University, New York, New York, United States of America Affiliation: Department of Biomedical Informatics, Columbia University, New York, New York, United States of America Affiliation: Department of Biomedical Informatics, Columbia University, New York, New York, United States of America Type 2 diabetes leads to premature death and reduced quality of life for 8% of Americans. Nutrition management is critical to maintaining glycemic control, yet it is difficult to achieve due to the high individual differences in glycemic response to nutrition. Anticipating glycemic impact of different meals can be challenging not only for individuals with diabetes, but also for expert diabetes educators. Personalized computational models that can accurately forecast an impact of a given meal on an individuals blood glucose levels can serve as the engine for a new generation of decision support tools for individuals with diabetes. However, to be useful in practice, these computational engines need to generate accurate forecasts based on limited datasets consistent with typical self-monitoring practices of individuals with type 2 diabetes. This paper uses three forecasting machines: (i) data assimilation, a technique borrowed from atmospheric phy Continue reading >>

Github - Jonneff/diabetes: A Brief Analysis Of The Aim94 Diabetes Dataset

Github - Jonneff/diabetes: A Brief Analysis Of The Aim94 Diabetes Dataset

A brief analysis of the AIM94 diabetes dataset If nothing happens, download GitHub Desktop and try again. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. This is a brief analysis of the AIM94 diabetes dataset. My objective in analyzing this dataset is to see if I can fit a simple linear model that will look for secular (consistently increasing or decreasing) trendds in the time series data. A secular trend in the wrong direction would indicate that the patient needs attention. The dataset analyzed here was collected for the 1994 AI in Medicine Symposium and is hosted at the UCI Irvine Machine Learning Repository. The dataset contains blood glucose and other measurements for 70 patients with Insulin Dependent Diabetes Mellitus (IDDM). The following excerpt explains the data codes in this set: "Diabetes patient records were obtained from two sources: an automatic electronic recording device and paper records. The automatic device had an internal clock to timestamp events, whereas the paper records only provided "logical time" slots (breakfast, lunch, dinner, bedtime). For paper records, fixed times were assigned to breakfast (08:00), lunch (12:00), dinner (18:00), and bedtime (22:00). Thus paper records have fictitious uniform recording times whereas electronic records have more realistic time stamps. Diabetes files consist of four fields per record. Each field is separated by a tab and each record is separated by a newline. So what we are dealing with here is time series data with somewhat fictitious uniform time stamps for part of the data set. The following excerpt is from the domain description provided for the symposium: "Pa Continue reading >>

Time-series Analysis Of Continuously Monitored Blood Glucose: The Impacts Of Geographic And Daily Lifestyle Factors

Time-series Analysis Of Continuously Monitored Blood Glucose: The Impacts Of Geographic And Daily Lifestyle Factors

Time-Series Analysis of Continuously Monitored Blood Glucose: The Impacts of Geographic and Daily Lifestyle Factors Sean T. Doherty 1and Stephen P. Greaves 2 1Department of Geography & Environmental Studies, Wilfrid Laurier University, 75 University Avenue West, Waterloo, ON, Canada N2L 3C5 2Institute of Transport and Logistics Studies, The University of Sydney Business School, University of Sydney, Sydney, NSW 2006, Australia Received 8 December 2014; Revised 13 March 2015; Accepted 16 March 2015 Copyright 2015 Sean T. Doherty and Stephen P. Greaves. This is an open access article distributed under the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Type 2 diabetes is known to be associated with environmental, behavioral, and lifestyle factors. However, the actual impacts of these factors on blood glucose (BG) variation throughout the day have remained relatively unexplored. Continuous blood glucose monitors combined with human activity tracking technologies afford new opportunities for exploration in a naturalistic setting. Data from a study of 40 patients with diabetes is utilized in this paper, including continuously monitored BG, food/medicine intake, and patient activity/location tracked using global positioning systems over a 4-day period. Standard linear regression and more disaggregated time-series analysis using autoregressive integrated moving average (ARIMA) are used to explore patient BG variation throughout the day and over space. The ARIMA models revealed a wide variety of BG correlating factors related to specific activity types, locations (especially those far from home), and travel modes, although the impacts were highly personal. Traditi Continue reading >>

Ohiot1dm Dataset

Ohiot1dm Dataset

The OhioT1DM dataset is available to researchers interested in improving the health and wellbeing of peoplewith type 1 diabetes. While it was originally developed for blood glucose level prediction, it is alsowell suited to other types of data analysis and knowledge discovery. For example, it could be used toexamine the effects of food or exercise or to consider whether phyiological signals from fitness bandsprovide actionable information. The OhioT1DM Dataset contains 8 weeks worth of data for each of 6 people with type 1 diabetes. These people were all on insulin pump therapy with continuous glucose monitoring (CGM). They reported life-event data and provided physiological data from a Basis Peak fitness band. The dataset includes: a CGM blood glucose level every 5 minutes; blood glucose levels from periodic self-monitoring of blood glucose (finger sticks); insulin doses, both bolus and basal; self-reported meal times with carbohydrate estimates; self-reported times of sleep, work, and exercise; and 5-minute aggregations of heart rate, galvanic skin response (GSR), skin temperature, air temperature, and step count. A paper fully describing the dataset is available: The OhioT1DM Dataset for Blood Glucose Level Prediction . To protect the data and ensure that it is used only for research purposes, a Data Use Agreement (DUA) is required. The DUA must be signed by a legal signatory for the research institution as well as by the principal investigator. Researchers can click here to Request the Data Use Agreement for the OhioT1DM Dataset . Once a DUA is executed, the dataset will be released, along with the OhioT1DM Viewer, a tool for data visualization. The OhioT1DM Dataset was first released for the Blood Glucose Level Prediction (BGLP) Challenge held at the Federated AI Continue reading >>

Working With Datasets

Working With Datasets

The Fitness REST API lets you create, obtain, and add points to datasets. Adataset represents a set of data points from a particular data source. Datasets are represented by the Users.dataSources.datasets resource. Important: For best practices when managing user data, see Responsible use of Google Fit . This example demonstrates how to add ten new step count delta points to apreviously empty dataset. This example assumes that you created a data sourceas described in Managing Data Sources . /datasets/1397513334728708316-1397515179728708316 { "dataSourceId": "derived:com.google.step_count.delta:1234567890:Example Manufacturer:ExampleTablet:1000001", "maxEndTimeNs": 1397515179728708316, "minStartTimeNs": 1397513334728708316, "point": [ { "dataTypeName": "com.google.step_count.delta", "endTimeNanos": 1397513365565713993, "originDataSourceId": "", "startTimeNanos": 1397513334728708316, "value": [ { "intVal": 8 } ] }, { "dataTypeName": "com.google.step_count.delta", "endTimeNanos": 1397513675197854515, "originDataSourceId": "", "startTimeNanos": 1397513530098955298, "value": [ { "intVal": 3 } ] }, { "dataTypeName": "com.google.step_count.delta", "endTimeNanos": 1397513764101240710, "originDataSourceId": "", "startTimeNanos": 1397513817073528237, "value": [ { "intVal": 6 } ] }, { "dataTypeName": "com.google.step_count.delta", "endTimeNanos": 1397513938674093579, "originDataSourceId": "", "startTimeNanos": 1397514015761859752, "value": [ { "intVal": 5 } ] }, { "dataTypeName": "com.google.step_count.delta", "endTimeNanos": 1397514106400006675, "originDataSourceId": "", "startTimeNanos": 1397514181893785805, "value": [ { "intVal": 4 } ] }, { "dataTypeName": "com.google.step_count.delta", "endTimeNanos": 1397514304850163634, "originDataSourceId": "", "startTimeNanos": 1397514356

Diabetes Prediction With Deep Learning Studio: A Different Approach Towards Deeplearning

Diabetes Prediction With Deep Learning Studio: A Different Approach Towards Deeplearning

Torture the data, and it will confess to anything. My professional portfolio website:- Diabetes Prediction with Deep Learning Studio: A Different approach towards DeepLearning Diabetes is a disease that occurs when your blood glucose, also called blood sugar, is too high. Blood glucose is your main source of energy and comes from the food you eat. Insulin, a hormone made by the pancreas, helps glucose from food get into your cells to be used for energy. Sometimes your body doesn't make enough or any insulin or doesn't use insulin well. Glucose then stays in your blood and doesn't reach your cells. Over time, having too much glucose in your blood can cause health problems. Although diabetes has no cure, so it is important to detect whether you have diabetes or not. In this article I will build a simple neural network to categorize given input into two classes, one that contains diabetes and other that doesn't, using Deep Learning Studio If you are not familiar with how to use Deep Learning Studio take a look at this:) Is Deep Learning without Programming Possible?medium.com This dataset is originally from the National Institute of Diabetes and Digestive and Kidney Diseases. The objective is to predict based on diagnostic measurements whether a patient has diabetes. Several constraints were placed on the selection of these instances from a larger database. In particular, all patients here are females at least 21 years old of Pima Indian heritage. Glucose: Plasma glucose concentration a 2 hours in an oral glucose tolerance test BloodPressure: Diastolic blood pressure (mm Hg) SkinThickness: Triceps skin fold thickness (mm) BMI: Body mass index (weight in kg/(height in m)) Diabetes Pedigree Function: Diabetes pedigree function Age: Age (years) Information: The Pima Indians Continue reading >>

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