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machine learning diabetes

Giada Acciaroli, Martina Vettoretti, Andrea Facchinetti, Giovanni Sparacino
Minimally invasive continuous glucose monitoring (CGM) sensors are wearable medical devices that provide real-time measurement of subcutaneous glucose concentration. This can be of great help in the daily management of diabetes. Most of the commercially available CGM devices have a wire-based sensor, usually placed in the subcutaneous tissue, which measures a "raw" current signal via a glucose-oxidase electrochemical reaction. This electrical signal needs to be translated in real-time to glucose concentration through a calibration process...
March 13, 2018: Biosensors
Allison Gates, Cydney Johnson, Lisa Hartling
BACKGROUND: Machine learning tools can expedite systematic review (SR) processes by semi-automating citation screening. Abstrackr semi-automates citation screening by predicting relevant records. We evaluated its performance for four screening projects. METHODS: We used a convenience sample of screening projects completed at the Alberta Research Centre for Health Evidence, Edmonton, Canada: three SRs and one descriptive analysis for which we had used SR screening methods...
March 12, 2018: Systematic Reviews
Piyush Samant, Ravinder Agarwal
BACKGROUND AND OBJECTIVE: Complementary and alternative medicine techniques have shown their potential for the treatment and diagnosis of chronical diseases like diabetes, arthritis etc. On the same time digital image processing techniques for disease diagnosis is reliable and fastest growing field in biomedical. Proposed model is an attempt to evaluate diagnostic validity of an old complementary and alternative medicine technique, iridology for diagnosis of type-2 diabetes using soft computing methods...
April 2018: Computer Methods and Programs in Biomedicine
Fatemeh Mokhtari, W Jack Rejeski, Yingying Zhu, Guorong Wu, Sean L Simpson, Jonathan H Burdette, Paul J Laurienti
More than one-third of adults in the United States are obese, with a higher prevalence among older adults. Obesity among older adults is a major cause of physical dysfunction, hypertension, diabetes, and coronary heart diseases. Many people who engage in lifestyle weight loss interventions fail to reach targeted goals for weight loss, and most will regain what was lost within 1-2 years following cessation of treatment. This variability in treatment efficacy suggests that there are important phenotypes predictive of success with intentional weight loss that could lead to tailored treatment regimen, an idea that is consistent with the concept of precision-based medicine...
February 19, 2018: NeuroImage
Olivier Simon, Rabi Yacoub, Sanjay Jain, John E Tomaszewski, Pinaki Sarder
We demonstrate a simple and effective automated method for the localization of glomeruli in large (~1 gigapixel) histopathological whole-slide images (WSIs) of thin renal tissue sections and biopsies, using an adaptation of the well-known local binary patterns (LBP) image feature vector to train a support vector machine (SVM) model. Our method offers high precision (>90%) and reasonable recall (>70%) for glomeruli from WSIs, is readily adaptable to glomeruli from multiple species, including mouse, rat, and human, and is robust to diverse slide staining methods...
February 1, 2018: Scientific Reports
Chengyin Ye, Tianyun Fu, Shiying Hao, Yan Zhang, Oliver Wang, Bo Jin, Minjie Xia, Modi Liu, Xin Zhou, Qian Wu, Yanting Guo, Chunqing Zhu, Yu-Ming Li, Devore S Culver, Shaun T Alfreds, Frank Stearns, Karl G Sylvester, Eric Widen, Doff McElhinney, Xuefeng Ling
BACKGROUND: As a high-prevalence health condition, hypertension is clinically costly, difficult to manage, and often leads to severe and life-threatening diseases such as cardiovascular disease (CVD) and stroke. OBJECTIVE: The aim of this study was to develop and validate prospectively a risk prediction model of incident essential hypertension within the following year. METHODS: Data from individual patient electronic health records (EHRs) were extracted from the Maine Health Information Exchange network...
January 30, 2018: Journal of Medical Internet Research
Dhariana Acón, Lihteh Wu
Throughout ophthalmic history it has been shown that progress has gone hand in hand with technological breakthroughs. In the past, fluorescein angiography and fundus photographs were the most commonly used imaging modalities in the management of diabetic macular edema (DME). Today, despite the moderate correlation between macular thickness and functional outcomes, spectral domain optical coherence tomography (SD-OCT) has become the DME workhorse in clinical practice. Several SD-OCT biomarkers have been looked at including presence of epiretinal membrane, vitreomacular adhesion, disorganization of the inner retinal layers, central macular thickness, integrity of the ellipsoid layer, and subretinal fluid, among others...
January 2018: Asia-Pacific Journal of Ophthalmology
V Buch, G Varughese, M Maruthappu
Medical artificial intelligence (AI) is moving forward at considerable pace. Promising research ideas are surfacing in clinical waters; AI is automating the national 111 triage service [1] and has exhibited dermatologist-level performance at identifying suspicious skin lesions, a task where experts frequently disagree [2]. The present article explores how machine learning, a prominent branch of AI, may be set to transform diabetes care. This article is protected by copyright. All rights reserved.
January 24, 2018: Diabetic Medicine: a Journal of the British Diabetic Association
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
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
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...
March 2018: Diabetes Care
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
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
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
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
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
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
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
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
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
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