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Diabetes machine learning

Vineeta Das, Samarendra Dandapat, Prabin Kumar Bora
Advancements in tele-medicine have led to the development of portable and cheap hand-held retinal imaging devices. However, the images obtained from these devices have low resolution (LR) and poor quality that may not be suitable for retinal disease diagnosis. Therefore, this paper proposes a novel framework for the super-resolution (SR) of the LR fundus images. The method takes into consideration the diagnostic information in the fundus images during the SR process. In this work, SR is performed on the zone of interest of the fundus images...
February 1, 2019: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
Yan Li, Foram Jasani, Dejun Su, Donglan Zhang, Lizheng Shi, Stella S Yi, José A Pagán
OBJECTIVE: Nearly one-third of adults in New York City (NYC) have high blood pressure and many social, economic, and behavioral factors may influence nonadherence to antihypertensive medication. The objective of this study is to identify profiles of adults who are not taking antihypertensive medications despite being advised to do so. METHODS: We used a machine learning-based population segmentation approach to identify population profiles related to nonadherence to antihypertensive medication...
January 2019: Journal of Primary Care & Community Health
Jeremy Pettus, Ronan Roussel, Fang Liz Zhou, Zsolt Bosnyak, Jukka Westerbacka, Rachele Berria, Javier Jimenez, Björn Eliasson, Irene Hramiak, Timothy Bailey, Luigi Meneghini
INTRODUCTION: The LIGHTNING study applied conventional and advanced analytic approaches to model, predict, and compare hypoglycemia rates of people with type 2 diabetes (T2DM) on insulin glargine 300 U/ml (Gla-300) with those on first-generation (insulin glargine 100 U/ml [Gla-100]; insulin detemir [IDet]) or second-generation (insulin degludec [IDeg]) basal-insulin (BI) analogs, utilizing a large real-world database. METHODS: Data were collected between 1 January 2007 and 31 March 2017 from the Optum Humedica US electronic health records [EHR] database...
February 14, 2019: Diabetes Therapy: Research, Treatment and Education of Diabetes and related Disorders
Zsolt Bosnyak, Fang Liz Zhou, Javier Jimenez, Rachele Berria
INTRODUCTION: Hypoglycemia remains a global burden and a limiting factor in the glycemic management of people with diabetes using basal insulins or oral antihyperglycemic drugs. Hypoglycemia data gleaned from randomized controlled trials (RCTs) have limited generalizability, as the strict RCT methodology and inclusion criteria do not fully reflect the real-world clinical picture. Therefore, real-world evidence, gathered from sources including electronic health records (EHR), is increasingly recognized as an important adjunct to RCTs...
February 14, 2019: Diabetes Therapy: Research, Treatment and Education of Diabetes and related Disorders
Lindsey Burggraaff, Paul Oranje, Robin Gouka, Pieter van der Pijl, Marian Geldof, Herman W T van Vlijmen, Adriaan P IJzerman, Gerard J P van Westen
Sodium-dependent glucose co-transporter 1 (SGLT1) is a solute carrier responsible for active glucose absorption. SGLT1 is present in both the renal tubules and small intestine. In contrast, the closely related sodium-dependent glucose co-transporter 2 (SGLT2), a protein that is targeted in the treatment of diabetes type II, is only expressed in the renal tubules. Although dual inhibitors for both SGLT1 and SGLT2 have been developed, no drugs on the market are targeted at decreasing dietary glucose uptake by SGLT1 in the gastrointestinal tract...
February 14, 2019: Journal of Cheminformatics
Michele Bernardini, Luca Romeo, Paolo Misericordia, Emanuele Frontoni
The diagnosis of Type 2 Diabetes (T2D) at an early stage has a key role for an adequate T2D integrated management system and patient's follow-up. Recent years have witnessed an increasing amount of available Electronic Health Record (EHR) data and Machine Learning (ML) techniques have been considerably evolving. However, managing and modeling this amount of information may lead to several challenges such as overfitting, model interpretability and computational cost. Starting from these motivations, we introduced a ML method called Sparse Balanced Support Vector Machine (SB-SVM) for discovering T2D in a novel collected EHR dataset (named FIMMG dataset)...
February 13, 2019: IEEE Journal of Biomedical and Health Informatics
Archana Sarda, Suresh Munuswamy, Shubhankar Sarda, Vinod Subramanian
BACKGROUND: Research studies are establishing the use of smartphone sensing to measure mental well-being. Smartphone sensor information captures behavioral patterns, and its analysis helps reveal well-being changes. Depression in diabetes goes highly underdiagnosed and underreported. The comorbidity has been associated with increased mortality and worse clinical outcomes, including poor glycemic control and self-management. Clinical-only intervention has been found to have a very modest effect on diabetes management among people with depression...
January 29, 2019: JMIR MHealth and UHealth
Jae Yung Kwon, Mohammad Ehsanul Karim, Maxim Topaz, Leanne M Currie
Although machine learning is increasingly being applied to support clinical decision making, there is a significant gap in understanding what it is and how nurses should adopt it in practice. The purpose of this case study is to show how one application of machine learning may support nursing work and to discuss how nurses can contribute to improving its relevance and performance. Using data from 130 specialized hospitals with 101 766 patients with diabetes, we applied various advanced statistical methods (known as machine learning algorithms) to predict early readmission...
January 25, 2019: Computers, Informatics, Nursing: CIN
Tahani A Daghistani, Radwa Elshawi, Sherif Sakr, Amjad M Ahmed, Abdullah Al-Thwayee, Mouaz H Al-Mallah
OBJECTIVE: The In-hospital length of stay (LOS) is expected to increase as cardiovascular diseases complexity increases and the population ages. This will affect healthcare systems especially with the current situation of decreased bed capacity and increasing costs. Therefore, accurately predicting LOS would have a positive impact on healthcare metrics. The aim of this study is to develop a machine learning-based model approach for predicting in-hospital LOS for cardiac patients. DESIGN: Using electronic medical records, we retrospectively extracted all records of patients' visits that were admitted under adult cardiology service...
January 19, 2019: International Journal of Cardiology
Hidayat Ullah, Tanzila Saba, Naveed Islam, Naveed Abbas, Amjad Rehman, Zahid Mehmood, Adeel Anjum
Atomic recognition of the Exudates (EXs), the major symbol of diabetic retinopathy is essential for automated retinal images analysis. In this article, we proposed a novel machine learning technique for early detection and classification of EXs in color fundus images. The major challenge observed in the classification technique is the selection of optimal features to reduce computational time and space complexity and to provide a high degree of classification accuracy. To address these challenges, this article proposed an evolutionary algorithm based solution for optimal feature selection, which accelerates the classification process and reduces computational complexity...
January 24, 2019: Microscopy Research and Technique
Ramu Adela, Podduturu Naveen Chander Reddy, Tarini Shankar Ghosh, Suruchi Aggarwal, Amit Kumar Yadav, Bhabatosh Das, Sanjay K Banerjee
BACKGROUND: Coronary artery disease (CAD) is the leading cause of morbidity and mortality in patients with type 2 diabetes mellitus (T2DM). The purpose of the present study was to discriminate the Indian CAD patients with or without T2DM by using multiple pathophysiological biomarkers. METHODS: Using sensitive multiplex protein assays, we assessed 46 protein markers including cytokines/chemokines, metabolic hormones, adipokines and apolipoproteins for evaluating different pathophysiological conditions of control, T2DM, CAD and T2DM with CAD patients (T2DM_CAD)...
January 24, 2019: Journal of Translational Medicine
Norman John Mapes, Christopher Rodriguez, Pradeep Chowriappa, Sumeet Dua
Free radicals that form from reactive species of nitrogen and oxygen can react dangerously with cellular components and are involved with the pathogenesis of diabetes, cancer, Parkinson's, and heart disease. Cysteine amino acids, due to their reactive nature, are prone to oxidation by these free radicals. Determining which cysteines oxidize within proteins is crucial to our understanding of these chronic diseases. Wet lab techniques, like differential alkylation, to determine which cysteines oxidize are often expensive and time-consuming...
2019: Computational and Structural Biotechnology Journal
Byoung Geol Choi, Seung Woon Rha, Suhng Wook Kim, Jun Hyuk Kang, Ji Young Park, Yung Kyun Noh
PURPOSE: Many studies have proposed predictive models for type 2 diabetes mellitus (T2DM). However, these predictive models have several limitations, such as user convenience and reproducibility. The purpose of this study was to develop a T2DM predictive model using electronic medical records (EMRs) and machine learning and to compare the performance of this model with traditional statistical methods. MATERIALS AND METHODS: In this study, a total of available 8454 patients who had no history of diabetes and were treated at the cardiovascular center of Korea University Guro Hospital were enrolled...
February 2019: Yonsei Medical Journal
Alexander Y Lau, Vincent Mok, Jack Lee, Yuhua Fan, Jinsheng Zeng, Bonnie Lam, Adrian Wong, Chloe Kwok, Maria Lai, Benny Zee
Objective: We investigated whether an automatic retinal image analysis (ARIA) incorporating machine learning approach can identify asymptomatic older adults harboring high burden of white matter hyperintensities (WMH) using MRI as gold standard. Methods: In this cross-sectional study, we evaluated 180 community-dwelling, stroke-, and dementia-free healthy subjects and performed ARIA by acquiring a nonmydriatic retinal fundus image. The primary outcome was the diagnostic performance of ARIA in detecting significant WMH on MRI brain, defined as age-related white matter changes (ARWMC) grade ≥2...
January 2019: Annals of Clinical and Translational Neurology
Oveeyen Moonian, Abha Jodheea-Jutton, Kavi Kumar Khedo, Shakuntala Baichoo, Soulakshmee Devi Nagowah, Leckraj Nagowah, Zahra Mungloo-Dilmohamud, Sudha Cheerkoot-Jalim
BACKGROUND: While healthcare systems are investing resources on type 2 diabetes patients, self-management is becoming the new trend for these patients. Due to the pervasiveness of computing devices, a number of computerized systems are emerging to support the self-management of patients. OBJECTIVE: The primary objective of this review is to identify and categorize the computational tools that exist for the self-management of type 2 diabetes, and to identify challenges that need to be addressed...
January 17, 2019: Informatics for Health & Social Care
Saifur R Khan, Haneesha Mohan, Ying Liu, Battsetseg Batchuluun, Himaben Gohil, Dana Al Rijjal, Yousef Manialawy, Brian J Cox, Erica P Gunderson, Michael B Wheeler
AIMS/HYPOTHESIS: Gestational diabetes mellitus (GDM) affects up to 20% of pregnancies, and almost half of the women affected progress to type 2 diabetes later in life, making GDM the most significant risk factor for the development of future type 2 diabetes. An accurate prediction of future type 2 diabetes risk in the early postpartum period after GDM would allow for timely interventions to prevent or delay type 2 diabetes. In addition, new targets for interventions may be revealed by understanding the underlying pathophysiology of the transition from GDM to type 2 diabetes...
January 15, 2019: Diabetologia
Philippe M Burlina, Neil Joshi, Katia D Pacheco, T Y Alvin Liu, Neil M Bressler
Importance: Deep learning (DL) used for discriminative tasks in ophthalmology, such as diagnosing diabetic retinopathy or age-related macular degeneration (AMD), requires large image data sets graded by human experts to train deep convolutional neural networks (DCNNs). In contrast, generative DL techniques could synthesize large new data sets of artificial retina images with different stages of AMD. Such images could enhance existing data sets of common and rare ophthalmic diseases without concern for personally identifying information to assist medical education of students, residents, and retinal specialists, as well as for training new DL diagnostic models for which extensive data sets from large clinical trials of expertly graded images may not exist...
January 10, 2019: JAMA Ophthalmology
Juleen Lam, Brian E Howard, Kristina Thayer, Ruchir R Shah
BACKGROUND: "Evidence Mapping" is an emerging tool that is increasingly being used to systematically identify, review, organize, quantify, and summarize the literature. It can be used as an effective method for identifying well-studied topic areas relevant to a broad research question along with any important literature gaps. However, because the procedure can be significantly resource-intensive, approaches that can increase the speed and reproducibility of evidence mapping are in great demand...
December 27, 2018: Environment International
Bhagaban Behera, Rathin Joshi, Anil Vishnu G K, Sanjay Bhalerao, Hardik J Pandya
In human-exhaled breath, more than 3000 volatile organic compounds (VOCs) are found which are directly or indirectly related to internal biochemical processes in the body. Electronic noses (E-noses) could play a potential role in screening/analyzing various respiratory and systemic diseases by studying breath signatures. E-nose integrates sensor array and an artificial neural network that responds to specific patterns of VOCs and thus can act as a non-invasive technology for disease monitoring. Gold standard blood glucose monitoring for diabetes diagnostics is invasive and highly uncomfortable...
January 8, 2019: Journal of Breath Research
Romany F Mansour
The high-pace rise in advanced computing and imaging systems has given rise to a new research dimension called computer-aided diagnosis (CAD) system for various biomedical purposes. CAD-based diabetic retinopathy (DR) can be of paramount significance to enable early disease detection and diagnosis decision. Considering the robustness of deep neural networks (DNNs) to solve highly intricate classification problems, in this paper, AlexNet DNN, which functions on the basis of convolutional neural network (CNN), has been applied to enable an optimal DR CAD solution...
February 2018: Biomedical Engineering Letters
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