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machine learning diabetes

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https://www.readbyqxmd.com/read/28880900/phase-separation-of-the-plasma-membrane-in-human-red-blood-cells-as-a-potential-tool-for-diagnosis-and-progression-monitoring-of-type-1-diabetes-mellitus
#1
Giuseppe Maulucci, Ermanno Cordelli, Alessandro Rizzi, Francesca De Leva, Massimiliano Papi, Gabriele Ciasca, Daniela Samengo, Giovambattista Pani, Dario Pitocco, Paolo Soda, Giovanni Ghirlanda, Giulio Iannello, Marco De Spirito
Glycosylation, oxidation and other post-translational modifications of membrane and transmembrane proteins can alter lipid density, packing and interactions, and are considered an important factor that affects fluidity variation in membranes. Red blood cells (RBC) membrane physical state, showing pronounced alterations in Type 1 diabetes mellitus (T1DM), could be the ideal candidate for monitoring the disease progression and the effects of therapies. On these grounds, the measurement of RBC membrane fluidity alterations can furnish a more sensitive index in T1DM diagnosis and disease progression than Glycosylated hemoglobin (HbA1c), which reflects only the information related to glycosylation processes...
2017: PloS One
https://www.readbyqxmd.com/read/28845459/moderate-exercise-has-limited-but-distinguishable-effects-on-the-mouse-microbiome
#2
Emily V Lamoureux, Scott A Grandy, Morgan G I Langille
The gut microbiome is known to have a complex yet vital relationship with host health. While both exercise and the gut microbiome have been shown to impact human health independently, the direct effects of moderate exercise on the intestinal microbiota remain unclear. In this study, we compared gut microbial diversity and changes in inflammatory markers associated with exercise over an 8-week period in mice that performed either voluntary exercise (VE) (n = 10) or moderate forced exercise (FE) (n = 11) and mice that did not perform any exercise (n = 21)...
July 2017: MSystems
https://www.readbyqxmd.com/read/28812204/multivariable-adaptive-artificial-pancreas-system-in-type-1-diabetes
#3
REVIEW
Ali Cinar
PURPOSE OF REVIEW: The review summarizes the current state of the artificial pancreas (AP) systems and introduces various new modules that should be included in future AP systems. RECENT FINDINGS: A fully automated AP must be able to detect and mitigate the effects of meals, exercise, stress and sleep on blood glucose concentrations. This can only be achieved by using a multivariable approach that leverages information from wearable devices that provide real-time streaming data about various physiological variables that indicate imminent changes in blood glucose concentrations caused by meals, exercise, stress and sleep...
August 15, 2017: Current Diabetes Reports
https://www.readbyqxmd.com/read/28803840/development-and-validation-of-risk-equations-for-complications-of-type-2-diabetes-recode-using-individual-participant-data-from-randomised-trials
#4
Sanjay Basu, Jeremy B Sussman, Seth A Berkowitz, Rodney A Hayward, John S Yudkin
BACKGROUND: In view of substantial mis-estimation of risks of diabetes complications using existing equations, we sought to develop updated Risk Equations for Complications Of type 2 Diabetes (RECODe). METHODS: To develop and validate these risk equations, we used data from the Action to Control Cardiovascular Risk in Diabetes study (ACCORD, n=9635; 2001-09) and validated the equations for microvascular events using data from the Diabetes Prevention Program Outcomes Study (DPPOS, n=1018; 1996-2001), and for cardiovascular events using data from the Action for Health in Diabetes (Look AHEAD, n=4760; 2001-12)...
August 10, 2017: Lancet Diabetes & Endocrinology
https://www.readbyqxmd.com/read/28791547/data-based-prediction-of-blood-glucose-concentrations-using-evolutionary-methods
#5
J Ignacio Hidalgo, J Manuel Colmenar, Gabriel Kronberger, Stephan M Winkler, Oscar Garnica, Juan Lanchares
Predicting glucose values on the basis of insulin and food intakes is a difficult task that people with diabetes need to do daily. This is necessary as it is important to maintain glucose levels at appropriate values to avoid not only short-term, but also long-term complications of the illness. Artificial intelligence in general and machine learning techniques in particular have already lead to promising results in modeling and predicting glucose concentrations. In this work, several machine learning techniques are used for the modeling and prediction of glucose concentrations using as inputs the values measured by a continuous monitoring glucose system as well as also previous and estimated future carbohydrate intakes and insulin injections...
August 8, 2017: Journal of Medical Systems
https://www.readbyqxmd.com/read/28746659/comparison-of-machine-learning-algorithms-to-build-a-predictive-model-for-detecting-undiagnosed-diabetes-elsa-brasil-accuracy-study
#6
André Rodrigues Olivera, Valter Roesler, Cirano Iochpe, Maria Inês Schmidt, Álvaro Vigo, Sandhi Maria Barreto, Bruce Bartholow Duncan
CONTEXT AND OBJECTIVE: Type 2 diabetes is a chronic disease associated with a wide range of serious health complications that have a major impact on overall health. The aims here were to develop and validate predictive models for detecting undiagnosed diabetes using data from the Longitudinal Study of Adult Health (ELSA-Brasil) and to compare the performance of different machine-learning algorithms in this task. DESIGN AND SETTING: Comparison of machine-learning algorithms to develop predictive models using data from ELSA-Brasil...
May 2017: São Paulo Medical Journal, Revista Paulista de Medicina
https://www.readbyqxmd.com/read/28738059/predicting-diabetes-mellitus-using-smote-and-ensemble-machine-learning-approach-the-henry-ford-exercise-testing-fit-project
#7
Manal Alghamdi, Mouaz Al-Mallah, Steven Keteyian, Clinton Brawner, Jonathan Ehrman, Sherif Sakr
Machine learning is becoming a popular and important approach in the field of medical research. In this study, we investigate the relative performance of various machine learning methods such as Decision Tree, Naïve Bayes, Logistic Regression, Logistic Model Tree and Random Forests for predicting incident diabetes using medical records of cardiorespiratory fitness. In addition, we apply different techniques to uncover potential predictors of diabetes. This FIT project study used data of 32,555 patients who are free of any known coronary artery disease or heart failure who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems between 1991 and 2009 and had a complete 5-year follow-up...
2017: PloS One
https://www.readbyqxmd.com/read/28711469/targeting-weight-loss-interventions-to-reduce-cardiovascular-complications-of-type-2-diabetes-a-machine-learning-based-post-hoc-analysis-of-heterogeneous-treatment-effects-in-the-look-ahead-trial
#8
Aaron Baum, Joseph Scarpa, Emilie Bruzelius, Ronald Tamler, Sanjay Basu, James Faghmous
BACKGROUND: The Action for Health in Diabetes (Look AHEAD) trial investigated whether long-term cardiovascular disease morbidity and mortality could be reduced through a weight loss intervention among people with type 2 diabetes. Despite finding no significant reduction in cardiovascular events on average, it is possible that some subpopulations might have derived benefit. In this post-hoc analysis, we test the hypothesis that the overall neutral average treatment effect in the trial masked important heterogeneous treatment effects (HTEs) from intensive weight loss interventions...
July 12, 2017: Lancet Diabetes & Endocrinology
https://www.readbyqxmd.com/read/28651852/differences-in-repolarization-heterogeneity-among-heart-failure-with-preserved-ejection-fraction-phenotypic-subgroups
#9
Suzanne K Oskouie, Stuart B Prenner, Sanjiv J Shah, Andrew J Sauer
Heart failure with preserved ejection fraction (HFpEF) is a highly heterogeneous syndrome associated with multiple medical comorbidities and pathophysiologic pathways or phenotypes. We recently developed a phenomapping method combining deep phenotyping with machine learning analysis to classify HFpEF patients into 3 clinically distinct phenotypic subgroups (phenogroups) with different clinical outcomes. Phenogroup #1 was younger with lower B-type natriuretic peptide levels, phenogroup #2 had the highest prevalence of obesity and diabetes mellitus, and phenogroup #3 was the oldest with the most factors for chronic kidney disease, the most dysfunctional myocardial mechanics, and the highest adverse outcomes...
August 15, 2017: American Journal of Cardiology
https://www.readbyqxmd.com/read/28626972/nirca-an-artificial-neural-network-based-insulin-resistance-calculator
#10
Konrad Stawiski, Iwona Pietrzak, Wojciech Młynarski, Wojciech Fendler, Agnieszka Szadkowska
BACKGROUND: Direct measurement of insulin sensitivity in children with type 1 diabetes is cumbersome and time consuming. OBJECTIVE: The aim of our study was to develop novel, accurate machine learning-based methods of insulin resistance estimation in children with type 1 diabetes. METHODS: A hyperinsulinemic hyperglycemic clamp study was performed to evaluate the glucose disposal rate (GDR) in a study group consisting of 315 patients aged 7...
June 19, 2017: Pediatric Diabetes
https://www.readbyqxmd.com/read/28597074/early-metabolic-markers-identify-potential-targets-for-the-prevention-of-type-2-diabetes
#11
Gopal Peddinti, Jeff Cobb, Loic Yengo, Philippe Froguel, Jasmina Kravić, Beverley Balkau, Tiinamaija Tuomi, Tero Aittokallio, Leif Groop
AIMS/HYPOTHESIS: The aims of this study were to evaluate systematically the predictive power of comprehensive metabolomics profiles in predicting the future risk of type 2 diabetes, and to identify a panel of the most predictive metabolic markers. METHODS: We applied an unbiased systems medicine approach to mine metabolite combinations that provide added value in predicting the future incidence of type 2 diabetes beyond known risk factors. We performed mass spectrometry-based targeted, as well as global untargeted, metabolomics, measuring a total of 568 metabolites, in a Finnish cohort of 543 non-diabetic individuals from the Botnia Prospective Study, which included 146 individuals who progressed to type 2 diabetes by the end of a 10 year follow-up period...
June 8, 2017: Diabetologia
https://www.readbyqxmd.com/read/28592309/machine-learning-techniques-for-diabetic-macular-edema-dme-classification-on-sd-oct-images
#12
Khaled Alsaih, Guillaume Lemaitre, Mojdeh Rastgoo, Joan Massich, Désiré Sidibé, Fabrice Meriaudeau
BACKGROUND: Spectral domain optical coherence tomography (OCT) (SD-OCT) is most widely imaging equipment used in ophthalmology to detect diabetic macular edema (DME). Indeed, it offers an accurate visualization of the morphology of the retina as well as the retina layers. METHODS: The dataset used in this study has been acquired by the Singapore Eye Research Institute (SERI), using CIRRUS TM (Carl Zeiss Meditec, Inc., Dublin, CA, USA) SD-OCT device. The dataset consists of 32 OCT volumes (16 DME and 16 normal cases)...
June 7, 2017: Biomedical Engineering Online
https://www.readbyqxmd.com/read/28569238/analyzing-breath-samples-of-hypoglycemic-events-in-type-1-diabetes-patients-towards-developing-an-alternative-to-diabetes-alert-dogs
#13
Amanda P Siegel, Ali Daneshkhah, Dana S Hardin, Sudhir Shrestha, Kody Varahramyan, Mangilal Agarwal
Diabetes is a disease that involves dysregulation of metabolic processes. Patients with type 1 diabetes (T1D) require insulin injections and measured food intake to maintain clinical stability, manually tracking their results by measuring blood glucose levels. Low blood glucose levels, hypoglycemia, can be extremely dangerous and can result in seizures, coma, or even death. Canines trained as diabetes alert dogs (DADs) have demonstrated the ability to detect hypoglycemia from breath, which led us to hypothesize that hypoglycemia, a metabolic dysregulation leading to low blood glucose levels, could be identified through analyzing volatile organic compounds (VOCs) contained within breath...
June 1, 2017: Journal of Breath Research
https://www.readbyqxmd.com/read/28569077/diabetes-and-prediabetes-classification-using-glycemic-variability-indices-from-continuous-glucose-monitoring-data
#14
Giada Acciaroli, Giovanni Sparacino, Liisa Hakaste, Andrea Facchinetti, Giorgio Maria Di Nunzio, Alessandro Palombit, Tiinamaija Tuomi, Rafael Gabriel, Jaime Aranda, Saturio Vega, Claudio Cobelli
BACKGROUND: Tens of glycemic variability (GV) indices are available in the literature to characterize the dynamic properties of glucose concentration profiles from continuous glucose monitoring (CGM) sensors. However, how to exploit the plethora of GV indices for classifying subjects is still controversial. For instance, the basic problem of using GV indices to automatically determine if the subject is healthy rather than affected by impaired glucose tolerance (IGT) or type 2 diabetes (T2D), is still unaddressed...
May 1, 2017: Journal of Diabetes Science and Technology
https://www.readbyqxmd.com/read/28508191/investigations-of-severity-level-measurements-for-diabetic-macular-oedema-using-machine-learning-algorithms
#15
S Murugeswari, R Sukanesh
BACKGROUND: The macula is an important part of the human visual system and is responsible for clear and colour vision. Macular oedema happens when fluid and protein deposit on or below the macula of the eye and cause the macula to thicken and swell. Normally, it occurs due to diabetes called diabetic macular oedema. Diabetic macular oedema (DME) is one of the main causes of visual impairment in patients. AIM: The aims of the present study are to detect and localize abnormalities in blood vessels with respect to macula in order to prevent vision loss for the diabetic patients...
May 15, 2017: Irish Journal of Medical Science
https://www.readbyqxmd.com/read/28503676/learning-optimal-individualized-treatment-rules-from-electronic-health-record-data
#16
Yuanjia Wang, Peng Wu, Ying Liu, Chunhua Weng, Donglin Zeng
Medical research is experiencing a paradigm shift from "one-size-fits-all" strategy to a precision medicine approach where the right therapy, for the right patient, and at the right time, will be prescribed. We propose a statistical method to estimate the optimal individualized treatment rules (ITRs) that are tailored according to subject-specific features using electronic health records (EHR) data. Our approach merges statistical modeling and medical domain knowledge with machine learning algorithms to assist personalized medical decision making using EHR...
October 2016: IEEE International Conference on Healthcare Informatics IEEE International Conference on Healthcare Informatics
https://www.readbyqxmd.com/read/28494618/machine-learning-methods-to-predict-diabetes-complications
#17
Arianna Dagliati, Simone Marini, Lucia Sacchi, Giulia Cogni, Marsida Teliti, Valentina Tibollo, Pasquale De Cata, Luca Chiovato, Riccardo Bellazzi
One of the areas where Artificial Intelligence is having more impact is machine learning, which develops algorithms able to learn patterns and decision rules from data. Machine learning algorithms have been embedded into data mining pipelines, which can combine them with classical statistical strategies, to extract knowledge from data. Within the EU-funded MOSAIC project, a data mining pipeline has been used to derive a set of predictive models of type 2 diabetes mellitus (T2DM) complications based on electronic health record data of nearly one thousand patients...
May 1, 2017: Journal of Diabetes Science and Technology
https://www.readbyqxmd.com/read/28455973/markers-of-arterial-health-could-serve-as-accurate-non-invasive-predictors-of-human-biological-and-chronological-age
#18
Alexander Fedintsev, Daria Kashtanova, Olga Tkacheva, Irina Strazhesko, Anna Kudryavtseva, Ancha Baranova, Alexey Moskalev
The decline in functional capacity is unavoidable consequence of the process of aging. While many anti-aging interventions have been proposed, clinical investigations into anti-aging medicine are limited by lack of reliable techniques for evaluating the rate of ageing. Here we present simple, accurate and cost-efficient techniques for estimation of human biological age, Male and Female Arterial Indices. We started with developing a model which accurately predicts chronological age. Using machine learning, we arrived on a set of four predictors, all of which reflect the functioning of the cardiovascular system...
April 2017: Aging
https://www.readbyqxmd.com/read/28433753/computational-image-analysis-for-prognosis-determination-in-dme
#19
Bianca S Gerendas, Hrvoje Bogunovic, Amir Sadeghipour, Thomas Schlegl, Georg Langs, Sebastian M Waldstein, Ursula Schmidt-Erfurth
In this pilot study, we evaluated the potential of computational image analysis of optical coherence tomography (OCT) data to determine the prognosis of patients with diabetic macular edema (DME). Spectral-domain OCT scans with fully automated retinal layer segmentation and segmentation of intraretinal cystoid fluid (IRC) and subretinal fluid of 629 patients receiving anti-vascular endothelial growth factor therapy for DME in a randomized prospective clinical trial were analyzed. The results were used to define 312 potentially predictive features at three timepoints (baseline, weeks 12 and 24) for best-corrected visual acuity (BCVA) at baseline and after one year used in a random forest prediction path...
April 19, 2017: Vision Research
https://www.readbyqxmd.com/read/28269690/smartsock-a-wearable-platform-for-context-aware-assessment-of-ankle-edema
#20
Ramin Fallahzadeh, Mahdi Pedram, Hassan Ghasemzadeh
Ankle edema an important symptom for monitoring patients with chronic systematic diseases. It is an important indicator of onset or exacerbation of a variety of diseases that disturb cardiovascular, renal, or hepatic system such as heart, liver, and kidney failure, diabetes, etc. The current approaches toward edema assessment are conducted during clinical visits. In-clinic assessments, in addition to being burdensome and expensive, are sometimes not reliable and neglect important contextual factors such as patient's physical activity level and body posture...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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