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https://www.readbyqxmd.com/read/29329721/quantifying-fluctuation-in-glucose-levels-to-identify-early-changes-in-glucose-homeostasis-in-cystic-fibrosis
#1
Rossa Brugha, Marie Wright, Suzie Nolan, Nicola Bridges, Siobhán B Carr
BACKGROUND: Cystic fibrosis related diabetes (CFRD) is associated with increased morbidity in CF. Variability in physiological systems is associated with dysfunctional homeostasis. We examined whether fluctuation in glucose is a marker of CFRD or "pre-diabetes". METHODS: Using a machine learning approach, we compared glucose IQR to current diagnostic criteria in a review of continuous glucose monitoring data. RESULTS: Analysis was performed on 248 studies from 142 children...
January 9, 2018: Journal of Cystic Fibrosis: Official Journal of the European Cystic Fibrosis Society
https://www.readbyqxmd.com/read/29297288/deep-learning-architectures-for-multi-label-classification-of-intelligent-health-risk-prediction
#2
Andrew Maxwell, Runzhi Li, Bei Yang, Heng Weng, Aihua Ou, Huixiao Hong, Zhaoxian Zhou, Ping Gong, Chaoyang Zhang
BACKGROUND: Multi-label classification of data remains to be a challenging problem. Because of the complexity of the data, it is sometimes difficult to infer information about classes that are not mutually exclusive. For medical data, patients could have symptoms of multiple different diseases at the same time and it is important to develop tools that help to identify problems early. Intelligent health risk prediction models built with deep learning architectures offer a powerful tool for physicians to identify patterns in patient data that indicate risks associated with certain types of chronic diseases...
December 28, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/29279299/characteristics-associated-with-decreased-or-increased-mortality-risk-from-glycemic-therapy-among-patients-with-type-2-diabetes-and-high-cardiovascular-risk-machine-learning-analysis-of-the-accord-trial
#3
Sanjay Basu, Sridharan Raghavan, Deborah J Wexler, Seth A Berkowitz
OBJECTIVE: Identifying patients who may experience decreased or increased mortality risk from intensive glycemic therapy for type 2 diabetes remains an important clinical challenge. We sought to identify characteristics of patients at high cardiovascular risk with decreased or increased mortality risk from glycemic therapy for type 2 diabetes using new methods to identify complex combinations of treatment effect modifiers. RESEARCH DESIGN AND METHODS: The machine learning method of gradient forest analysis was applied to understand the variation in all-cause mortality within the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial (N = 10,251), whose participants were 40-79 years old with type 2 diabetes, hemoglobin A1c (HbA1c) ≥7...
December 26, 2017: Diabetes Care
https://www.readbyqxmd.com/read/29249829/a-polygenic-score-for-schizophrenia-predicts-glycemic-control
#4
REVIEW
Han Cao, Junfang Chen, Andreas Meyer-Lindenberg, Emanuel Schwarz
Schizophrenia is substantially comorbid with type 2 diabetes (T2D), but the molecular basis of this effect is incompletely understood. Here, we show that a cortical schizophrenia expression score predicts glycemic control from pancreatic islet cell expression. We used machine learning to identify a cortical expression signature in 212 schizophrenia patients and controls, which explained ~25% of the illness-associated variance. The algorithm was predicted in expression data from 51 subjects (9 with T2D), explained up to 26...
December 18, 2017: Translational Psychiatry
https://www.readbyqxmd.com/read/29234807/development-and-validation-of-a-deep-learning-system-for-diabetic-retinopathy-and-related-eye-diseases-using-retinal-images-from-multiethnic-populations-with-diabetes
#5
COMPARATIVE STUDY
Daniel Shu Wei Ting, Carol Yim-Lui Cheung, Gilbert Lim, Gavin Siew Wei Tan, Nguyen D Quang, Alfred Gan, Haslina Hamzah, Renata Garcia-Franco, Ian Yew San Yeo, Shu Yen Lee, Edmund Yick Mun Wong, Charumathi Sabanayagam, Mani Baskaran, Farah Ibrahim, Ngiap Chuan Tan, Eric A Finkelstein, Ecosse L Lamoureux, Ian Y Wong, Neil M Bressler, Sobha Sivaprasad, Rohit Varma, Jost B Jonas, Ming Guang He, Ching-Yu Cheng, Gemmy Chui Ming Cheung, Tin Aung, Wynne Hsu, Mong Li Lee, Tien Yin Wong
Importance: A deep learning system (DLS) is a machine learning technology with potential for screening diabetic retinopathy and related eye diseases. Objective: To evaluate the performance of a DLS in detecting referable diabetic retinopathy, vision-threatening diabetic retinopathy, possible glaucoma, and age-related macular degeneration (AMD) in community and clinic-based multiethnic populations with diabetes. Design, Setting, and Participants: Diagnostic performance of a DLS for diabetic retinopathy and related eye diseases was evaluated using 494 661 retinal images...
December 12, 2017: JAMA: the Journal of the American Medical Association
https://www.readbyqxmd.com/read/29218909/reading-between-the-genes-computational-models-to-discover-function-from-noncoding-dna
#6
Yves A Lussier, Joanne Berghout, Francesca Vitali, Kenneth S Ramos, Maricel Kann, Jason H Moore
Noncoding DNA - once called "junk" has revealed itself to be full of function. Technology development has allowed researchers to gather genome-scale data pointing towards complex regulatory regions, expression and function of noncoding RNA genes, and conserved elements. Variation in these regions has been tied to variation in biological function and human disease. This PSB session tackles the problem of handling, analyzing and interpreting the data relating to variation in and interactions between noncoding regions through computational biology...
2018: Pacific Symposium on Biocomputing
https://www.readbyqxmd.com/read/29181232/machine-learning-to-compare-frequent-medical-problems-of-african-american-and-caucasian-diabetic-kidney-patients
#7
Yong-Mi Kim, Pranay Kathuria, Dursun Delen
Objectives: End-stage renal disease (ESRD), which is primarily a consequence of diabetes mellitus, shows an exemplary health disparity between African American and Caucasian patients in the United States. Because diabetic chronic kidney disease (CKD) patients of these two groups show differences in their medical problems, the markers leading to ESRD are also expected to differ. The purpose of this study was, therefore, to compare their medical complications at various levels of kidney function and to identify markers that can be used to predict ESRD...
October 2017: Healthcare Informatics Research
https://www.readbyqxmd.com/read/29180800/electronic-health-record-driven-prediction-for-gestational-diabetes-mellitus-in-early-pregnancy
#8
Hang Qiu, Hai-Yan Yu, Li-Ya Wang, Qiang Yao, Si-Nan Wu, Can Yin, Bo Fu, Xiao-Juan Zhu, Yan-Long Zhang, Yong Xing, Jun Deng, Hao Yang, Shun-Dong Lei
Gestational diabetes mellitus (GDM) is conventionally confirmed with oral glucose tolerance test (OGTT) in 24 to 28 weeks of gestation, but it is still uncertain whether it can be predicted with secondary use of electronic health records (EHRs) in early pregnancy. To this purpose, the cost-sensitive hybrid model (CSHM) and five conventional machine learning methods are used to construct the predictive models, capturing the future risks of GDM in the temporally aggregated EHRs. The experimental data sources from a nested case-control study cohort, containing 33,935 gestational women in West China Second Hospital...
November 27, 2017: Scientific Reports
https://www.readbyqxmd.com/read/29124453/a-machine-learning-ensemble-classifier-for-early-prediction-of-diabetic-retinopathy
#9
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
#10
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
#11
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
#12
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
#13
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
#14
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
#15
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
#16
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
#17
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
#18
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
#19
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
#20
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
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