The prevalence of prediabetes is rapidly increasing, and this can lead to an increased risk for individuals to develop type 2 diabetes and associated diseases. Therefore, it is necessary to develop nutritional strategies to maintain healthy glucose l...
INTRODUCTION: Recent studies have reported that HbA1c and lipid variability is useful for risk stratification in diabetes mellitus. The present study evaluated the predictive value of the baseline, subsequent mean of at least three measurements and v...
AMIA ... Annual Symposium proceedings. AMIA Symposium
Jan 25, 2021
Our previous research shows that structured cancer DX description data accuracy varied across electronic health record (EHR) segments (e.g. encounter DX, problem list, etc.). We provide initial evidence corroborating these findings in EHRs from patie...
Journal of diabetes science and technology
Oct 22, 2020
BACKGROUND: A risk assessment tool has been developed for automated estimation of level of neuropathy based on the clinical characteristics of patients. The smart tool is based on risk factors for diabetic neuropathy, which utilizes vibration percept...
Retinal blood vessels provide information on the risk of cardiovascular disease (CVD). Here, we report the development and validation of deep-learning models for the automated measurement of retinal-vessel calibre in retinal photographs, using divers...
BACKGROUND: Gestational diabetes mellitus (GDM) contributes to adverse pregnancy and birth outcomes. In recent decades, extensive research has been devoted to the early prediction of GDM by various methods. Machine learning methods are flexible predi...
IEEE journal of biomedical and health informatics
Jan 17, 2020
[Formula: see text] is a primary marker of long-term average blood glucose, which is an essential measure of successful control in type 1 diabetes. Previous studies have shown that [Formula: see text] estimates can be obtained from 5-12 weeks of dail...
Despite effective medications, rates of uncontrolled glucose levels in type 2 diabetes remain high. We aimed to test the utility of machine learning applied to big data in identifying the potential role of concomitant drugs not taken for diabetes whi...
BMC medical informatics and decision making
Nov 15, 2019
BACKGROUND: Electronic medical records (EMR) contain numerical data important for clinical outcomes research, such as vital signs and cardiac ejection fractions (EF), which tend to be embedded in narrative clinical notes. In current practice, this da...
AIM: To assess the potential of supervised machine-learning techniques to identify clinical variables for predicting short-term and long-term glycated haemoglobin (HbA1c) response after insulin treatment initiation in patients with type 2 diabetes me...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.