AIMC Topic: Diabetes Mellitus, Type 2
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DETERMINATION OF THE PRA POSITIVITY PERCENTAGE IN MALE PATIENTS WITH CHRONIC KIDNEY DISEASE BY USING FLOW CYTOMETRY TECHNIQUE.
Acta clinica Croatica
Aug 1, 2023
The antibodies directed against human leukocyte antigen (HLA) molecules, which play a crucial role in allograft histocompatibility, are called anti-HLA antibodies. Anti-HLA antibodies against foreign HLA molecules may be present in patients with chro...
Deep Learning Algorithm Detects Presence of Disorganization of Retinal Inner Layers (DRIL)-An Early Imaging Biomarker in Diabetic Retinopathy.
Translational vision science & technology
Jul 3, 2023
PURPOSE: To develop and train a deep learning-based algorithm for detecting disorganization of retinal inner layers (DRIL) on optical coherence tomography (OCT) to screen a cohort of patients with diabetic retinopathy (DR).
Predicting Long-Term Type 2 Diabetes with Artificial Intelligence (AI): A Scoping Review.
Studies in health technology and informatics
Jun 29, 2023
Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder that affects a significant portion of the global population. Artificial intelligence (AI) has emerged as a promising tool for predicting T2DM risk. To provide an overview of the AI techn...
A knowledge-based decision support system for inferring supportive treatment recommendations for diabetes mellitus.
Technology and health care : official journal of the European Society for Engineering and Medicine
Jan 1, 2023
BACKGROUND: Diabetes Mellitus (DM) is a significant risk, mostly causing blindness, kidney failure, heart attack, stroke, and lower limb amputation. A Clinical Decision Support System (CDSS) can assist healthcare practitioners in their daily effort a...
TVAR: assessing tissue-specific functional effects of non-coding variants with deep learning.
Bioinformatics (Oxford, England)
Oct 14, 2022
MOTIVATION: Analysis of whole-genome sequencing (WGS) for genetics is still a challenge due to the lack of accurate functional annotation of non-coding variants, especially the rare ones. As eQTLs have been extensively implicated in the genetics of h...
Clinical significance of hepatic function in Graves disease with type 2 diabetic mellitus: A single-center retrospective cross-sectional study in Taiwan.
Medicine
Aug 26, 2022
Graves disease (GD) and type 2 diabetes mellitus (T2DM) both impair liver function; we therefore explored the possibility of a relationship among diabetic control, thyroid function, and liver function. This retrospective, cross-sectional study compar...
Glucose trajectory prediction by deep learning for personal home care of type 2 diabetes mellitus: modelling and applying.
Mathematical biosciences and engineering : MBE
Jul 18, 2022
Glucose management for people with type 2 diabetes mellitus is essential but challenging due to the multi-factored and chronic disease nature of diabetes. To control glucose levels in a safe range and lessen abnormal glucose variability efficiently a...
Identifying daily activities of patient work for type 2 diabetes and co-morbidities: a deep learning and wearable camera approach.
Journal of the American Medical Informatics Association : JAMIA
Jul 12, 2022
OBJECTIVE: People are increasingly encouraged to self-manage their chronic conditions; however, many struggle to practise it effectively. Most studies that investigate patient work (ie, tasks involved in self-management and contexts influencing such ...
Characterization of Type 2 Diabetes Using Counterfactuals and Explainable AI.
Studies in health technology and informatics
May 25, 2022
Type 2 diabetes mellitus is a metabolic disorder of glucose management, whose prevalence is increasing inexorably worldwide. Adherence to therapies, along with a healthy lifestyle can help prevent the onset of disease. This preliminary study proposes...
Vec2image: an explainable artificial intelligence model for the feature representation and classification of high-dimensional biological data by vector-to-image conversion.
Briefings in bioinformatics
Mar 10, 2022
Feature representation and discriminative learning are proven models and technologies in artificial intelligence fields; however, major challenges for machine learning on large biological datasets are learning an effective model with mechanistical ex...
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