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Diabetes machine learning

Arunava Chakravarty, Jayanthi Sivaswamy
BACKGROUND AND OBJECTIVE: Accurate segmentation of the intra-retinal tissue layers in Optical Coherence Tomography (OCT) images plays an important role in the diagnosis and treatment of ocular diseases such as Age-Related Macular Degeneration (AMD) and Diabetic Macular Edema (DME). The existing energy minimization based methods employ multiple, manually handcrafted cost terms and often fail in the presence of pathologies. In this work, we eliminate the need to handcraft the energy by learning it from training images in an end-to-end manner...
October 2018: Computer Methods and Programs in Biomedicine
Geon Kim, YoungJu Jo, Hyungjoo Cho, Hyun-Seok Min, YongKeun Park
We present a rapid and label-free method for hematologic screening for diseases and syndromes, utilizing quantitative phase imaging (QPI) and machine learning. We aim to establish an efficient blood examination framework that does not suffer from the drawbacks of conventional blood assays, which are incapable of profiling single cells or require labeling procedures. Our method involves the synergistic employment of QPI and machine learning. The high-dimensional refractive index information arising from the QPI-based profiling of single red blood cells is processed to screen for diseases and syndromes using machine learning, which can utilize high-dimensional data beyond the human level...
September 21, 2018: Biosensors & Bioelectronics
Edgar Guevara, Juan Carlos Torres-Galván, Miguel G Ramírez-Elías, Claudia Luevano-Contreras, Francisco Javier González
Type 2 diabetes mellitus (DM2) is one of the most widely prevalent diseases worldwide and is currently screened by invasive techniques based on enzymatic assays that measure plasma glucose concentration in a laboratory setting. A promising plan of action for screening DM2 is to identify molecular signatures in a non-invasive fashion. This work describes the application of portable Raman spectroscopy coupled with several supervised machine-learning techniques, to discern between diabetic patients and healthy controls (Ctrl), with a high degree of accuracy...
October 1, 2018: Biomedical Optics Express
David J Albers, Matthew E Levine, Andrew Stuart, Lena Mamykina, Bruce Gluckman, George Hripcsak
We introduce data assimilation as a computational method that uses machine learning to combine data with human knowledge in the form of mechanistic models in order to forecast future states, to impute missing data from the past by smoothing, and to infer measurable and unmeasurable quantities that represent clinically and scientifically important phenotypes. We demonstrate the advantages it affords in the context of type 2 diabetes by showing how data assimilation can be used to forecast future glucose values, to impute previously missing glucose values, and to infer type 2 diabetes phenotypes...
October 1, 2018: Journal of the American Medical Informatics Association: JAMIA
Idan Hecht, Asaf Bar, Lior Rokach, Romi Noy Achiron, Marion R Munk, Wolfgang Huf, Zvia Burgansky-Eliash, Asaf Achiron
PURPOSE: In diabetic patients presenting with macular edema (ME) shortly after cataract surgery, identifying the underlying pathology can be challenging and influence management. Our aim was to develop a simple clinical classifier able to confirm a diabetic etiology using few spectral domain optical coherence tomography parameters. METHODS: We analyzed spectral domain optical coherence tomography data of 153 patients with either pseudophakic cystoid ME (n = 57), diabetic ME (n = 86), or "mixed" (n = 10)...
October 3, 2018: Retina
Sahar Ashrafzadeh, Osama Hamdy
The growing burden of diabetes is fueled by obesity-inducing lifestyle behaviors including high-calorie diets and lack of physical activity. Challenges in access to diabetes specialists and educators, low adherence to medications, and inadequate motivational support for proper disease self-management contribute to poor glycemic control in patients with diabetes. Simultaneously, high patient volumes and low reimbursement rates limit physicians' time spent on lifestyle behavior counseling. These barriers to efficient diabetes care lead to high rates of diabetes-related complications, driving healthcare costs up and reducing the quality of patients' lives...
September 20, 2018: Cell Metabolism
Md Mohaiminul Islam, Ye Tian, Yan Cheng, Yang Wang, Pingzhao Hu
Background: Epigenetic modification has an effect on gene expression under the environmental alteration, but it does not change corresponding genome sequence. DNA methylation (DNAm) is one of the important epigenetic mechanisms. DNAm variations could be used as epigenetic markers to predict and account for the change of many human phenotypic traits, such as cancer, diabetes, and high blood pressure. In this study, we built deep neural network (DNN) regression models to account for interindividual variation in triglyceride concentrations measured at different visits of peripheral blood samples using epigenome-wide DNAm profiles...
2018: BMC Proceedings
Andrea S Martinez-Vernon, James A Covington, Ramesh P Arasaradnam, Siavash Esfahani, Nicola O'Connell, Ioannis Kyrou, Richard S Savage
MOTIVATION: The measurement of disease biomarkers in easily-obtained bodily fluids has opened the door to a new type of non-invasive medical diagnostics. New technologies are being developed and fine-tuned in order to make this possibility a reality. One such technology is Field Asymmetric Ion Mobility Spectrometry (FAIMS), which allows the measurement of volatile organic compounds (VOCs) in biological samples such as urine. These VOCs are known to contain a range of information on the relevant person's metabolism and can in principle be used for disease diagnostic purposes...
2018: PloS One
J Balani, S L Hyer, H Shehata, F Mohareb
Objective: To develop a model to predict gestational diabetes mellitus incorporating classical and a novel risk factor, visceral fat mass. Methods: Three hundred two obese non-diabetic pregnant women underwent body composition analysis at booking by bioimpedance analysis. Of this cohort, 72 (24%) developed gestational diabetes mellitus. Principal component analysis was initially performed to identify possible clustering of the gestational diabetes mellitus and non-GDM groups...
September 2018: Obstetric Medicine
Che Ngufor, Holly Van Houten, Brian S Caffo, Nilay D Shah, Rozalina G McCoy
Accurate and reliable prediction of clinical progression over time has the potential to improve the outcomes of chronic disease. The classical approach to analyzing longitudinal data is to use (generalized) linear mixed-effect models (GLMM). However, linear parametric models are predicated on assumptions, which are often difficult to verify. In contrast, data-driven machine learning methods can be applied to derive insight from the raw data without a priori assumptions. However, the underlying theory of most machine learning algorithms assume that the data is independent and identically distributed, making them inefficient for longitudinal supervised learning...
September 3, 2018: Journal of Biomedical Informatics
Jan Budzianowski, Jarosław Hiczkiewicz, Paweł Burchardt, Konrad Pieszko, Janusz Rzeźniczak, Paweł Budzianowski, Katarzyna Korybalska
Inflammation, oxidative stress, myocardial injury biomarkers and clinical parameters (longer AF duration, left atrial enlargement, the metabolic syndrome) are factors commonly related to AF recurrence. This study aims to assess the predictive value of laboratory and clinical parameters responsible for early recurrence of atrial fibrillation (ERAF) following cryoballoon ablation (CBA) using statistical assessment and machine learning algorithms. This study group comprised 118 consecutive patients (mean age, 62...
August 23, 2018: Heart and Vessels
Yukai Li, Huling Li, Hua Yao
The focus of this study is the use of machine learning methods that combine feature selection and imbalanced process (SMOTE algorithm) to classify and predict diabetes follow-up control satisfaction data. After the feature selection and unbalanced process, diabetes follow-up data of the New Urban Area of Urumqi, Xinjiang, was used as input variables of support vector machine (SVM), decision tree, and integrated learning model (Adaboost and Bagging) for modeling and prediction. The experimental results show that Adaboost algorithm produces better classification results...
2018: Computational and Mathematical Methods in Medicine
Jean Debédat, Nataliya Sokolovska, Muriel Coupaye, Simona Panunzi, Rima Chakaroun, Laurent Genser, Garance de Turenne, Jean-Luc Bouillot, Christine Poitou, Jean-Michel Oppert, Matthias Blüher, Michael Stumvoll, Geltrude Mingrone, Séverine Ledoux, Jean-Daniel Zucker, Karine Clément, Judith Aron-Wisnewsky
OBJECTIVE: Roux-en-Y gastric bypass (RYGB) induces type 2 diabetes remission (DR) in 60% of patients at 1 year, yet long-term relapse occurs in half of these patients. Scoring methods to predict DR outcomes 1 year after surgery that include only baseline parameters cannot accurately predict 5-year DR (5y-DR). We aimed to develop a new score to better predict 5y-DR. RESEARCH DESIGN AND METHODS: We retrospectively included 175 RYGB patients with type 2 diabetes with 5-year follow-up...
October 2018: Diabetes Care
Yuheng Chen, Wenna Duan, Parshant Sehrawat, Vaibhav Chauhan, Freddy J Alfaro, Anna Gavrieli, Xingye Qiao, Vera Novak, Weiying Dai
BACKGROUND: Type 2 diabetes mellitus (T2DM) is associated with alterations in the blood-brain barrier, neuronal damage, and arterial stiffness, thus affecting cerebral metabolism and perfusion. There is a need to implement machine-learning methodologies to identify a T2DM-related perfusion pattern and possible relationship between the pattern and cognitive performance/disease severity. PURPOSE: To develop a machine-learning pipeline to investigate the method's discriminative value between T2DM patients and normal controls, the T2DM-related network pattern, and association of the pattern with cognitive performance/disease severity...
August 5, 2018: Journal of Magnetic Resonance Imaging: JMRI
Maryna Neborachko, Aleksandr Pkhakadze, Iryna Vlasenko
Industry 4.0 is an updated concept of smart production, which is identified with the fourth industrial revolution and the emergence of cyber-physical systems. Industry 4.0 is the next stage in the digitization of productions and industries, where such technologies and concepts as the Internet of things, big data, predictive analytics, cloud computing, machine learning, machine interaction, artificial intelligence, robotics, 3D printing, augmented reality. As an area of therapy with the best market potential and one of the most expensive global diseases, diabetes attracts the best healthcare players, who use innovative technologies...
July 29, 2018: Diabetes & Metabolic Syndrome
Ursula Schmidt-Erfurth, Amir Sadeghipour, Bianca S Gerendas, Sebastian M Waldstein, Hrvoje Bogunović
Major advances in diagnostic technologies are offering unprecedented insight into the condition of the retina and beyond ocular disease. Digital images providing millions of morphological datasets can fast and non-invasively be analyzed in a comprehensive manner using artificial intelligence (AI). Methods based on machine learning (ML) and particularly deep learning (DL) are able to identify, localize and quantify pathological features in almost every macular and retinal disease. Convolutional neural networks thereby mimic the path of the human brain for object recognition through learning of pathological features from training sets, supervised ML, or even extrapolation from patterns recognized independently, unsupervised ML...
August 1, 2018: Progress in Retinal and Eye Research
Amrita Roy Chowdhury, Tamojit Chatterjee, Sreeparna Banerjee
Classification of abnormalities from medical images using computer-based approaches is of growing interest in medical imaging. Timely detection of abnormalities due to diabetic retinopathy and age-related macular degeneration is required in order to prevent the prognosis of the disease. Computer-aided systems using machine learning are becoming interesting to ophthalmologists and researchers. We present here one such technique, the Random Forest classifier, to aid medical practitioners in accurate diagnosis of the diseases...
August 4, 2018: Medical & Biological Engineering & Computing
Zhaohan Xiong, Tong Liu, Gary Tse, Mengqi Gong, Patrick A Gladding, Bruce H Smaill, Martin K Stiles, Anne M Gillis, Jichao Zhao
Background: Meta-analysis is a widely used tool in which weighted information from multiple similar studies is aggregated to increase statistical power. However, the exponential growth of publications in key areas of medical science has rendered manual identification of relevant studies increasingly time-consuming. The aim of this work was to develop a machine learning technique capable of robust automatic study selection for meta-analysis. We have validated this approach with an up-to-date meta-analysis to investigate the association between diabetes mellitus (DM) and new-onset atrial fibrillation (AF)...
2018: Frontiers in Physiology
Shaik Mohammad Naushad, Tajamul Hussain, Bobbala Indumathi, Khatoon Samreen, Salman A Alrokayan, Vijay Kumar Kutala
In view of high mortality associated with coronary artery disease (CAD), development of an early predicting tool will be beneficial in reducing the burden of the disease. The database comprising demographic, conventional, folate/xenobiotic genetic risk factors of 648 subjects (364 cases of CAD and 284 healthy controls) was used as the basis to develop CAD risk and percentage stenosis prediction models using ensemble machine learning algorithms (EMLA), multifactor dimensionality reduction (MDR) and recursive partitioning (RP)...
July 11, 2018: Molecular Biology Reports
Wen Cao, Nicholas Czarnek, Juan Shan, Lin Li
Diabetic retinopathy (DR) is an eye abnormality caused by long-term diabetes and it is the most common cause of blindness before the age of 50. Microaneurysms (MAs), resulting from leakage from retinal blood vessels, are early indicators of DR. In this paper, we analyzed MA detectability using small 25 by 25 pixel patches extracted from fundus images in the DIAbetic RETinopathy DataBase - Calibration Level 1 (DIARETDB1). Raw pixel intensities of extracted patches served directly as inputs into the following classifiers: random forest (RF), neural network, and support vector machine...
July 2018: IEEE Transactions on Nanobioscience
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