Diabetes machine learning

The objective of this review was to critically examine existing digital applications, tailored for use by citizens and professionals, to provide diet monitoring, diet planning, and precision nutrition. We sought to identify the strengths and weaknesses of such digital applications, while exploring their potential contributions to enhancing public health, and discussed potential developmental pathways. Nutrition is a critical aspect of maintaining good health, with an unhealthy diet being one of the primary risk factors for chronic diseases, such as obesity, diabetes, and cardiovascular disease...

BACKGROUND: Cardiometabolic disorders pose significant health risks globally. Metabolic syndrome, characterized by a cluster of potentially reversible metabolic abnormalities, is a known risk factor for these disorders. Early detection and intervention for individuals with metabolic abnormalities can help mitigate the risk of developing more serious cardiometabolic conditions. This study aimed to develop an image-derived phenotype (IDP) for metabolic abnormality from unenhanced abdominal computed tomography (CT) scans using deep learning...

PURPOSE: In early 2022, a fluorescein shortage occurred in the United States. To meet the standard of care for patients who required ultra-wide fundus fluorescein angiography (UWFFA), a regimen of half-dose (250mg) sodium fluorescein (10%) was adopted instead of the full-dose (500mg) at the Cole Eye Institute (CEI). In this paper, we compare the image quality, clinical utility, and the side effect profile of half-dose vs full-dose fluorescein in UWFFA for a cohort of stable patients. DESIGN: Retrospective chart review...

Diabetes Mellitus is one of the oldest diseases known to humankind, dating back to ancient Egypt. The disease is a chronic metabolic disorder that heavily burdens healthcare providers worldwide due to the steady increment of patients yearly. Worryingly, diabetes affects not only the aging population but also children. It is prevalent to control this problem, as diabetes can lead to many health complications. As evolution happens, humankind starts integrating computer technology with the healthcare system. The utilization of artificial intelligence assists healthcare to be more efficient in diagnosing diabetes patients, better healthcare delivery, and more patient eccentric...

The increasing need for precise dietary monitoring across various health scenarios has led to innovations in wearable sensing technologies. However, continuously tracking food and fluid intake during daily activities can be complex. In this study, we present a machine-learning-powered smart neckband that features wireless connectivity and a comfortable, foldable design. Initially considered beneficial for managing conditions such as diabetes and obesity by facilitating dietary control, the device's utility extends beyond these applications...

Cardiovascular and cerebrovascular diseases (CCVD) are prominent mortality causes in Japan, necessitating effective preventative measures, early diagnosis, and treatment to mitigate their impact. A diagnostic model was developed to identify patients with ischemic heart disease (IHD), stroke, or both, using specific health examination data. Lifestyle habits affecting CCVD development were analyzed using five causal inference methods. This study included 473,734 patients aged ≥ 40 years who underwent specific health examinations in Kanazawa, Japan between 2009 and 2018 to collect data on basic physical information, lifestyle habits, and laboratory parameters such as diabetes, lipid metabolism, renal function, and liver function...

OBJECTIVES: To estimate the incremental medical cost of diabetes mellitus using information from administrative databases in Colombia. METHODS: We carried out a retrospective cohort study with administrative health databases from Colombian population affiliated in the contributory health insurance scheme. We used an operative definition to select the cohort with diabetes. Incremental cost and cost ratio of diabetes were estimated using an inverse probability weighting of treatment approach to find the causal effect of having the disease...

ACKGROUND: Diabetes mellitus (DM) frequently results in Diabetic Nephropathy (DN), which has a significant negative impact on the quality of life of diabetic patients. Sphingolipid metabolism is associated with diabetes, but its relationship with DN is unclear. Therefore, screening biomarkers related to sphingolipid metabolism is crucial for treating DN. METHODS: To identify Differentially Expressed Genes (DEGs) in the GSE142153 dataset, we conducted a differential expression analysis (DN samples versus control samples)...

To assess the feasibility of code-free deep learning (CFDL) platforms in the prediction of binary outcomes from fundus images in ophthalmology, evaluating two distinct online-based platforms (Google Vertex and Amazon Rekognition), and two distinct datasets. Two publicly available datasets, Messidor-2 and BRSET, were utilized for model development. The Messidor-2 consists of fundus photographs from diabetic patients and the BRSET is a multi-label dataset. The CFDL platforms were used to create deep learning models, with no preprocessing of the images, by a single ophthalmologist without coding expertise...

Type 2 Diabetes (T2D) exerts a substantial impact on mortality rates. According to 2023 statistics, more than half a billion individuals are experiencing the effects of T2D, making it one of the top 10 leading contributors to worldwide deaths. Multiple factors contribute to the onset of T2D, such as obesity, poor diet and lifestyle, the mutation in specific genes and many more. Among the various factors that contribute to the development of T2D, genetics is a pivotal aspect. Due to the significant influence of genes in the initiation and advancement of various phases of T2D, our focus lies on exploring the association between T2D and genes...

OBJECTIVE: To utilize radiomics analysis on dual-energy CT images of the pancreas to establish a quantitative imaging biomarker for type 2 diabetes mellitus. MATERIALS AND METHODS: In this retrospective study, 78 participants (45 with type 2 diabetes mellitus, 33 without) underwent a dual energy CT exam. Pancreas regions were segmented automatically using a deep learning algorithm. From these regions, radiomics features were extracted. Additionally, 24 clinical features were collected for each patient...

BACKGROUND AND OBJECTIVE: Type 2 Diabetes Mellitus (T2DM) with insulin resistance (IR) is prone to damage the vascular endothelial, leading to the formation of vulnerable carotid plaques and increasing ischemic stroke (IS) risk. The purpose of this study is to develop a nomogram model based on carotid ultrasound radiomics for predicting IS risk in T2DM patients. METHODS: 198 T2DM patients were enrolled and separated into study and control groups based on IS history...

BACKGROUND: Type 2 Diabetes Mellitus (T2DM) presents a significant healthcare challenge, with considerable economic ramifications. While blood glucose management and long-term metabolic target setting for home care and outpatient treatment follow established procedures, the approach for short-term targets during hospitalization varies due to a lack of clinical consensus. Our study aims to elucidate the impact of pre-hospitalization and intra-hospitalization glycemic indexes on in-hospital survival rates in individuals with T2DM, addressing this notable gap in the current literature...

BACKGROUND: The occurrence of pressure injury in patients with diabetes during ICU hospitalization can result in severe complications, including infections and non-healing wounds. AIMS: The aim of this study was to predict the occurrence of pressure injury in ICU patients with diabetes using machine learning models. STUDY DESIGN: In this study, LASSO regression was used for feature screening, XGBoost was employed for machine learning model construction, ROC curve analysis, calibration curve analysis, clinical decision curve analysis, sensitivity, specificity, accuracy, and F1 score were used for evaluating the model's performance...

INTRODUCTION: Obesity and gender play a critical role in shaping the outcomes of COVID-19 disease. These two factors have a dynamic relationship with each other, as well as other risk factors, which hinders interpretation of how they influence severity and disease progression. This work aimed to study differences in COVID-19 disease outcomes through analysis of risk profiles stratified by gender and obesity status. METHODS: This study employed an unsupervised clustering analysis, using Mexico's national COVID-19 hospitalization dataset, which contains demographic information and health outcomes of patients hospitalized due to COVID-19...

Diabetic retinopathy (DR) is the leading cause of visual impairment globally. It occurs due to long-term diabetes with fluctuating blood glucose levels. It has become a significant concern for people in the working age group as it can lead to vision loss in the future. Manual examination of fundus images is time-consuming and requires much effort and expertise to determine the severity of the retinopathy. To diagnose and evaluate the disease, deep learning-based technologies have been used, which analyze blood vessels, microaneurysms, exudates, macula, optic discs, and hemorrhages also used for initial detection and grading of DR...

OBJECTIVE: Diabetic retinopathy is one of the major complications of diabetes. In this study, a diabetic retinopathy risk prediction model integrating machine learning models and SHAP was established to increase the accuracy of risk prediction for diabetic retinopathy, explain the rationality of the findings from model prediction and improve the reliability of prediction results. METHODS: Data were preprocessed for missing values and outliers, features selected through information gain, a diabetic retinopathy risk prediction model established using the CatBoost and the outputs of the mode interpreted using the SHAP model...

OBJECTIVE: There is limited consensus regarding management of spinal epidural abscesses (SEA), particularly in patients without neurologic deficits. Several models have been created to predict failure of medical management in patients with SEA. We evaluate the external validity of five predictive models in an independent cohort of patients with SEA. METHODS: 176 patients with SEA between 2010 and 2019 at our institution were identified, and variables relevant to each predictive model were collected...

BACKGROUND: The rapidly increasing availability of medical data in electronic health records (EHRs) may contribute to the concept of learning health systems, allowing for better personalized care. Type 2 diabetes mellitus was chosen as the use case in this study. OBJECTIVE: This study aims to explore the applicability of a recently developed patient similarity-based analytics approach based on EHRs as a candidate data analytical decision support tool. METHODS: A previously published precision cohort analytics workflow was adapted for the Dutch primary care setting using EHR data from the Nivel Primary Care Database...

PURPOSE: To assess the capabilities of Chat Generative Pre-trained Transformer (ChatGPT) and Vertex AI in executing code-free preprocessing, training machine learning (ML) models, and analyzing the data. DESIGN: Evaluation of diagnostic test or technology. PARTICIPANTS: ChatGPT and Vetrex AI as publicly available large language model and ML platform, respectively. METHODS: ChatGPT was employed to improve the resolution of fundus photography images from the Methods to Evaluate Segmentation and Indexing Techniques in the field of Retinal Ophthalmology (Messidor-2) open-source dataset using the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique by Fiji software...