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https://www.readbyqxmd.com/read/29124453/a-machine-learning-ensemble-classifier-for-early-prediction-of-diabetic-retinopathy
#1
Somasundaram S K, Alli P
The main complication of diabetes is Diabetic retinopathy (DR), retinal vascular disease and it leads to the blindness. Regular screening for early DR disease detection is considered as an intensive labor and resource oriented task. Therefore, automatic detection of DR diseases is performed only by using the computational technique is the great solution. An automatic method is more reliable to determine the presence of an abnormality in Fundus images (FI) but, the classification process is poorly performed...
November 9, 2017: Journal of Medical Systems
https://www.readbyqxmd.com/read/29095564/a-risk-score-of-bmi-hba1c-and-triglycerides-predicts-future-glycemic-control-in-type-2-diabetes
#2
Dorijn Fl Hertroijs, Arianne Mj Elissen, Martijn Cgj Brouwers, Nicolaas C Schaper, Sebastian Köhler, Mirela C Popa, Stylianos Asteriadis, Steven H Hendriks, Henk J Bilo, Dirk Ruwaard
AIM: To identify, predict and validate distinct glycemic trajectories among patients with newly diagnosed type 2 diabetes treated in primary care, as a first step towards more effective patient-centred care. MATERIAL AND METHODS: We conducted a retrospective study on two cohorts using routinely collected individual patient data in primary care practices from two large Dutch diabetes patient registries. Participants included newly diagnosed, adult patients with type 2 diabetes between January 2006 and December 2014 (n = 10,528, development cohort; n = 3,777, validation cohort)...
November 2, 2017: Diabetes, Obesity & Metabolism
https://www.readbyqxmd.com/read/29060555/learning-about-individuals-health-from-aggregate-data
#3
Rich Colbaugh, Kristin Glass
There is growing awareness that user-generated social media content contains valuable health-related information and is more convenient to collect than typical health data. For example, Twitter has been employed to predict aggregate-level outcomes, such as regional rates of diabetes and child poverty, and to identify individual cases of depression and food poisoning. Models which make aggregate-level inferences can be induced from aggregate data, and consequently are straightforward to build. In contrast, learning models that produce individual-level (IL) predictions, which are more informative, usually requires a large number of difficult-to-acquire labeled IL examples...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/29060208/data-driven-strategies-for-robust-forecast-of-continuous-glucose-monitoring-time-series
#4
Samuele Fiorini, Chiara Martini, Davide Malpassi, Renzo Cordera, Davide Maggi, Alessandro Verri, Annalisa Barla
Over the past decade, continuous glucose monitoring (CGM) has proven to be a very resourceful tool for diabetes management. To date, CGM devices are employed for both retrospective and online applications. Their use allows to better describe the patients' pathology as well as to achieve a better control of patients' level of glycemia. The analysis of CGM sensor data makes possible to observe a wide range of metrics, such as the glycemic variability during the day or the amount of time spent below or above certain glycemic thresholds...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/29059959/retinal-hemorrhage-detection-by-rule-based-and-machine-learning-approach
#5
Di Xiao, Shuang Yu, Janardhan Vignarajan, Dong An, Mei-Ling Tay-Kearney, Yogi Kanagasingam
Robust detection of hemorrhages (HMs) in color fundus image is important in an automatic diabetic retinopathy grading system. Detection of the hemorrhages that are close to or connected with retinal blood vessels was found to be challenge. However, most methods didn't put research on it, even some of them mentioned this issue. In this paper, we proposed a novel hemorrhage detection method based on rule-based and machine learning methods. We focused on the improvement of detection of the hemorrhages that are close to or connected with retinal blood vessels, besides detecting the independent hemorrhage regions...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/29054258/comparative-approaches-for-classification-of-diabetes-mellitus-data-machine-learning-paradigm
#6
Md Maniruzzaman, Nishith Kumar, Md Menhazul Abedin, Md Shaykhul Islam, Harman S Suri, Ayman S El-Baz, Jasjit S Suri
BACKGROUND AND OBJECTIVE: Diabetes is a silent killer. The main cause of this disease is the presence of excessive amounts of metabolites such as glucose. There were about 387 million diabetic people all over the world in 2014. The financial burden of this disease has been calculated to be about $13,700 per year. According to the World Health Organization (WHO), these figures will more than double by the year 2030. This cost will be reduced dramatically if someone can predict diabetes statistically on the basis of some covariates...
December 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/29016439/examining-the-ability-of-artificial-neural-networks-machine-learning-models-to-accurately-predict-complications-following-posterior-lumbar-spine-fusion
#7
Jun S Kim, Robert K Merrill, Varun Arvind, Deepak Kaji, Sara D Pasik, Chuma C Nwachukwu, Luilly Vargas, Nebiyu S Osman, Eric K Oermann, John M Caridi, Samuel K Cho
STUDY DESIGN: Cross-sectional database study. OBJECTIVE: To train and validate machine learning models to identify risk factors for complications following posterior lumbar spine fusion. SUMMARY OF BACKGROUND DATA: Machine learning models such as artificial neural networks (ANNs) are valuable tools for analyzing and interpreting large and complex datasets. ANNs have yet to be used for risk factor analysis in orthopedic surgery. METHODS: The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was queried for patients who underwent posterior lumbar spine fusion...
October 9, 2017: Spine
https://www.readbyqxmd.com/read/28979001/a-machine-learning-heuristic-to-improve-gene-score-prediction-of-polygenic-traits
#8
Guillaume Paré, Shihong Mao, Wei Q Deng
Machine-learning techniques have helped solve a broad range of prediction problems, yet are not widely used to build polygenic risk scores for the prediction of complex traits. We propose a novel heuristic based on machine-learning techniques (GraBLD) to boost the predictive performance of polygenic risk scores. Gradient boosted regression trees were first used to optimize the weights of SNPs included in the score, followed by a novel regional adjustment for linkage disequilibrium. A calibration set with sample size of ~200 individuals was sufficient for optimal performance...
October 4, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28968709/integrating-regulatory-features-data-for-prediction-of-functional-disease-associated-snps
#9
Shan-Shan Dong, Yan Guo, Shi Yao, Yi-Xiao Chen, Mo-Nan He, Yu-Jie Zhang, Xiao-Feng Chen, Jia-Bin Chen, Tie-Lin Yang
Genome-wide association studies (GWASs) are an effective strategy to identify susceptibility loci for human complex diseases. However, missing heritability is still a big problem. Most GWASs single-nucleotide polymorphisms (SNPs) are located in noncoding regions, which has been considered to be the unexplored territory of the genome. Recently, data from the Encyclopedia of DNA Elements (ENCODE) and Roadmap Epigenomics projects have shown that many GWASs SNPs in the noncoding regions fall within regulatory elements...
August 16, 2017: Briefings in Bioinformatics
https://www.readbyqxmd.com/read/28943335/single-nucleotide-polymorphism-relevance-learning-with-random-forests-for-type-2-diabetes-risk-prediction
#10
Beatriz López, Ferran Torrent-Fontbona, Ramón Viñas, José Manuel Fernández-Real
OBJECTIVE: The use of artificial intelligence techniques to find out which Single Nucleotide Polymorphisms (SNPs) promote the development of a disease is one of the features of medical research, as such techniques may potentially aid early diagnosis and help in the prescription of preventive measures. In particular, the aim is to help physicians to identify the relevant SNPs related to Type 2 diabetes, and to build a decision-support tool for risk prediction. METHODS: We use the Random Forest (RF) technique in order to search for the most important attributes (SNPs) related to diabetes, giving a weight (degree of importance), ranging between 0 and 1, to each attribute...
September 21, 2017: Artificial Intelligence in Medicine
https://www.readbyqxmd.com/read/28929121/differential-and-combined-effects-of-physical-activity-profiles-and-prohealth-behaviors-on-diabetes-prevalence-among-blacks-and-whites-in-the-us-population-a-novel-bayesian-belief-network-machine-learning-analysis
#11
Azizi A Seixas, Dwayne A Henclewood, Aisha T Langford, Samy I McFarlane, Ferdinand Zizi, Girardin Jean-Louis
The current study assessed the prevalence of diabetes across four different physical activity lifestyles and infer through machine learning which combinations of physical activity, sleep, stress, and body mass index yield the lowest prevalence of diabetes in Blacks and Whites. Data were extracted from the National Health Interview Survey (NHIS) dataset from 2004-2013 containing demographics, chronic diseases, and sleep duration (N = 288,888). Of the total sample, 9.34% reported diabetes (where the prevalence of diabetes was 12...
2017: Journal of Diabetes Research
https://www.readbyqxmd.com/read/28922296/trajectories-of-glycemic-change-in-a-national-cohort-of-adults-with-previously-controlled-type-2-diabetes
#12
Rozalina G McCoy, Che Ngufor, Holly K Van Houten, Brian Caffo, Nilay D Shah
BACKGROUND: Individualized diabetes management would benefit from prospectively identifying well-controlled patients at risk of losing glycemic control. OBJECTIVES: To identify patterns of hemoglobin A1c (HbA1c) change among patients with stable controlled diabetes. RESEARCH DESIGN: Cohort study using OptumLabs Data Warehouse, 2001-2013. We develop and apply a machine learning framework that uses a Bayesian estimation of the mixture of generalized linear mixed effect models to discover glycemic trajectories, and a random forest feature contribution method to identify patient characteristics predictive of their future glycemic trajectories...
November 2017: Medical Care
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
#13
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
#14
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
#15
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
#16
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
#17
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
#18
COMPARATIVE STUDY
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
#19
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
#20
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
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