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https://www.readbyqxmd.com/read/29789037/a-comparison-of-statistical-and-machine-learning-techniques-in-evaluating-the-association-between-dietary-patterns-and-10-year-cardiometabolic-risk-2002-2012-the-attica-study
#1
Dimitris Panaretos, Efi Koloverou, Alexandros C Dimopoulos, Georgia-Maria Kouli, Malvina Vamvakari, George Tzavelas, Christos Pitsavos, Demosthenes B Panagiotakos
Statistical methods are usually applied in examining diet-disease associations, whereas factor analysis is commonly used for dietary pattern recognition. Recently, machine learning (ML) has been also proposed as an alternative technique in health classification. In this work, the predictive accuracy of statistical v. ML methodologies as regards the association of dietary patterns on CVD risk was tested. During 2001-2002, 3042 men and women (45 (sd 14) years) were enrolled in the ATTICA study. In 2011-2012, the 10-year CVD follow-up was performed among 2020 participants...
May 23, 2018: British Journal of Nutrition
https://www.readbyqxmd.com/read/29778925/deep-learning-strategy-for-accurate-carotid-intima-media-thickness-measurement-an-ultrasound-study-on-japanese-diabetic-cohort
#2
Mainak Biswas, Venkatanareshbabu Kuppili, Tadashi Araki, Damodar Reddy Edla, Elisa Cuadrado Godia, Luca Saba, Harman S Suri, Tomaž Omerzu, John R Laird, Narendra N Khanna, Andrew Nicolaides, Jasjit S Suri
MOTIVATION: The carotid intima-media thickness (cIMT) is an important biomarker for cardiovascular diseases and stroke monitoring. This study presents an intelligence-based, novel, robust, and clinically-strong strategy that uses a combination of deep-learning (DL) and machine-learning (ML) paradigms. METHODOLOGY: A two-stage DL-based system (a class of AtheroEdge™ systems) was proposed for cIMT measurements. Stage I consisted of a convolution layer-based encoder for feature extraction and a fully convolutional network-based decoder for image segmentation...
May 12, 2018: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/29727291/machine-learning-based-automatic-neovascularization-detection-on-optic-disc-region
#3
Shuang Yu, Di Xiao, Yogesan Kanagasingam
In this paper, the automatic detection of neovascularization in the optic disc region (NVD) for color fundus retinal image is presented. NV is the indicator for the onset of proliferative diabetic retinopathy and it is featured by the presence of new vessels in the retina. The new vessels are fragile and pose a high risk for sudden vision loss. Therefore, the importance of accurate and timely detection of NV cannot be underestimated. We propose an automatic image processing procedure for NVD detection that involves vessel segmentation using multilevel Gabor filtering, feature extraction of vessel morphological features and texture features, and image classification with support vector machine...
May 2018: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/29718387/investigating-predictors-of-cognitive-decline-using-machine-learning
#4
Ramon Casanova, Santiago Saldana, Michael W Lutz, Brenda L Plassman, Maragatha Kuchibhatla, Kathleen M Hayden
Objectives: Genetic risks for cognitive decline are not modifiable; however their relative importance compared to modifiable factors is unclear. We used machine learning to evaluate modifiable and genetic risk factors for Alzheimer's disease(AD), to predict cognitive decline. Methods: Health and Retirement Study participants, aged 65-90, with DNA and >2 cognitive evaluations, were included (n=7,142). Predictors included age, body mass index, gender, education, APOE ε4, CVD, hypertension, diabetes, stroke, neighborhood socio-economic status(NSES), and AD risk genes...
April 27, 2018: Journals of Gerontology. Series B, Psychological Sciences and Social Sciences
https://www.readbyqxmd.com/read/29704086/automated-quality-assessment-of-colour-fundus-images-for-diabetic-retinopathy-screening-in-telemedicine
#5
Sajib Kumar Saha, Basura Fernando, Jorge Cuadros, Di Xiao, Yogesan Kanagasingam
Fundus images obtained in a telemedicine program are acquired at different sites that are captured by people who have varying levels of experience. These result in a relatively high percentage of images which are later marked as unreadable by graders. Unreadable images require a recapture which is time and cost intensive. An automated method that determines the image quality during acquisition is an effective alternative. To determine the image quality during acquisition, we describe here an automated method for the assessment of image quality in the context of diabetic retinopathy...
April 27, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29679305/the-new-possibilities-from-big-data-to-overlooked-associations-between-diabetes-biochemical-parameters-glucose-control-and-osteoporosis
#6
REVIEW
Christian Kruse
PURPOSE OF REVIEW: To review current practices and technologies within the scope of "Big Data" that can further our understanding of diabetes mellitus and osteoporosis from large volumes of data. "Big Data" techniques involving supervised machine learning, unsupervised machine learning, and deep learning image analysis are presented with examples of current literature. RECENT FINDINGS: Supervised machine learning can allow us to better predict diabetes-induced osteoporosis and understand relative predictor importance of diabetes-affected bone tissue...
April 20, 2018: Current Osteoporosis Reports
https://www.readbyqxmd.com/read/29650030/harnessing-qatar-biobank-to-understand-type-2-diabetes-and-obesity-in-adult-qataris-from-the-first-qatar-biobank-project
#7
Ehsan Ullah, Raghvendra Mall, Reda Rawi, Naima M Moustaid, Adeel A Butt, Halima Bensmail
BACKGROUND: Human tissues are invaluable resources for researchers worldwide. Biobanks are repositories of such human tissues and can have a strategic importance for genetic research, clinical care, and future discoveries and treatments. One of the aims of Qatar Biobank is to improve the understanding and treatment of common diseases afflicting Qatari population such as obesity and diabetes. METHODS: In this study we apply a panorama of state-of-the-art statistical methods and machine learning algorithms to investigate associations and risk factors for diabetes and obesity on a sample of 1000 Qatari population...
April 12, 2018: Journal of Translational Medicine
https://www.readbyqxmd.com/read/29629449/post-hoc-support-vector-machine-learning-for-impedimetric-biosensors-based-on-weak-protein-ligand-interactions
#8
Y Rong, A V Padron, K J Hagerty, N Nelson, S Chi, N O Keyhani, J Katz, S P A Datta, C Gomes, E S McLamore
Impedimetric biosensors for measuring small molecules based on weak/transient interactions between bioreceptors and target analytes are a challenge for detection electronics, particularly in field studies or in the analysis of complex matrices. Protein-ligand binding sensors have enormous potential for biosensing, but achieving accuracy in complex solutions is a major challenge. There is a need for simple post hoc analytical tools that are not computationally expensive, yet provide near real time feedback on data derived from impedance spectra...
April 9, 2018: Analyst
https://www.readbyqxmd.com/read/29568746/metagenomics-biomarkers-selected-for-prediction-of-three-different-diseases-in-chinese-population
#9
Honglong Wu, Lihua Cai, Dongfang Li, Xinying Wang, Shancen Zhao, Fuhao Zou, Ke Zhou
The dysbiosis of human microbiome has been proven to be associated with the development of many human diseases. Metagenome sequencing emerges as a powerful tool to investigate the effects of microbiome on diseases. Identification of human gut microbiome markers associated with abnormal phenotypes may facilitate feature selection for multiclass classification. Compared with binary classifiers, multiclass classification models deploy more complex discriminative patterns. Here, we developed a pipeline to address the challenging characterization of multilabel samples...
2018: BioMed Research International
https://www.readbyqxmd.com/read/29549733/combining-deep-residual-neural-network-features-with-supervised-machine-learning-algorithms-to-classify-diverse-food-image-datasets
#10
Patrick McAllister, Huiru Zheng, Raymond Bond, Anne Moorhead
Obesity is increasing worldwide and can cause many chronic conditions such as type-2 diabetes, heart disease, sleep apnea, and some cancers. Monitoring dietary intake through food logging is a key method to maintain a healthy lifestyle to prevent and manage obesity. Computer vision methods have been applied to food logging to automate image classification for monitoring dietary intake. In this work we applied pretrained ResNet-152 and GoogleNet convolutional neural networks (CNNs), initially trained using ImageNet Large Scale Visual Recognition Challenge (ILSVRC) dataset with MatConvNet package, to extract features from food image datasets; Food 5K, Food-11, RawFooT-DB, and Food-101...
April 1, 2018: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/29548646/grader-variability-and-the-importance-of-reference-standards-for-evaluating-machine-learning-models-for-diabetic-retinopathy
#11
Jonathan Krause, Varun Gulshan, Ehsan Rahimy, Peter Karth, Kasumi Widner, Greg S Corrado, Lily Peng, Dale R Webster
PURPOSE: Use adjudication to quantify errors in diabetic retinopathy (DR) grading based on individual graders and majority decision, and to train an improved automated algorithm for DR grading. DESIGN: Retrospective analysis. PARTICIPANTS: Retinal fundus images from DR screening programs. METHODS: Images were each graded by the algorithm, U.S. board-certified ophthalmologists, and retinal specialists. The adjudicated consensus of the retinal specialists served as the reference standard...
March 2, 2018: Ophthalmology
https://www.readbyqxmd.com/read/29534053/calibration-of-minimally-invasive-continuous-glucose-monitoring-sensors-state-of-the-art-and-current-perspectives
#12
REVIEW
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
https://www.readbyqxmd.com/read/29530097/technology-assisted-title-and-abstract-screening-for-systematic-reviews-a-retrospective-evaluation-of-the-abstrackr-machine-learning-tool
#13
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
https://www.readbyqxmd.com/read/29477420/machine-learning-techniques-for-medical-diagnosis-of-diabetes-using-iris-images
#14
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
https://www.readbyqxmd.com/read/29471100/dynamic-fmri-networks-predict-success-in-a-behavioral-weight-loss-program-among-older-adults
#15
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...
June 2018: NeuroImage
https://www.readbyqxmd.com/read/29391542/multi-radial-lbp-features-as-a-tool-for-rapid-glomerular-detection-and-assessment-in-whole-slide-histopathology-images
#16
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
https://www.readbyqxmd.com/read/29382633/prediction-of-incident-hypertension-within-the-next-year-prospective-study-using-statewide-electronic-health-records-and-machine-learning
#17
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
https://www.readbyqxmd.com/read/29376234/multimodal-imaging-in-diabetic-macular-edema
#18
REVIEW
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
https://www.readbyqxmd.com/read/29368355/artificial-intelligence-in-diabetes-care
#19
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
https://www.readbyqxmd.com/read/29329721/quantifying-fluctuation-in-glucose-levels-to-identify-early-changes-in-glucose-homeostasis-in-cystic-fibrosis
#20
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
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