AIMC Topic: Diabetes Mellitus, Type 2

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An innovative method for screening and evaluating the degree of diabetic retinopathy and drug treatment based on artificial intelligence algorithms.

Pharmacological research Jun 2, 2020
Current methods of evaluating the degree of diabetic retinopathy are highly subjective and have no quantitative standard. To objectively evaluate the slight changes in tissue structure during the early stage of retinal diseases, a subjective interpre...
Diabetes Mellitus, Type 2 Mice Rats, Wistar Diabetes Mellitus, Type 1 Hypoglycemic Agents Image Interpretation, Computer-Assisted Animals Predictive Value of Tests Early Diagnosis Diabetic Retinopathy Male Drugs, Chinese Herbal Severity of Illness Index Retinal Ganglion Cells Diabetes Mellitus, Experimental Deep Learning Pattern Recognition, Automated Retina Retinal Vessels Microscopy
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Oral microbiome-systemic link studies: perspectives on current limitations and future artificial intelligence-based approaches.

Critical reviews in microbiology May 21, 2020
In the past decade, there has been a tremendous increase in studies on the link between oral microbiome and systemic diseases. However, variations in study design and confounding variables across studies often lead to inconsistent observations. In th...
Atherosclerosis Microbiota Mouth Metagenomics Disease Pregnancy Humans Female Diagnosis Neural Networks, Computer Diabetes Mellitus, Type 2 Artificial Intelligence Arthritis, Rheumatoid Pancreatic Neoplasms
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Machine-learning based exploration of determinants of gray matter volume in the KORA-MRI study.

Scientific reports May 20, 2020
To identify the most important factors that impact brain volume, while accounting for potential collinearity, we used a data-driven machine-learning approach. Gray Matter Volume (GMV) was derived from magnetic resonance imaging (3T, FLAIR) and adjust...
Humans Machine Learning Prospective Studies Magnetic Resonance Imaging Models, Neurological Organ Size Linear Models Germany Middle Aged Gray Matter Case-Control Studies Prediabetic State Diabetes Mellitus, Type 2 Life Style Glomerular Filtration Rate Confounding Factors, Epidemiologic Female Follow-Up Studies Cross-Sectional Studies Male Cardiovascular Diseases
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Natural Language Processing Improves Detection of Nonsevere Hypoglycemia in Medical Records Versus Coding Alone in Patients With Type 2 Diabetes but Does Not Improve Prediction of Severe Hypoglycemia Events: An Analysis Using the Electronic Medical Record in a Large Health System.

Diabetes care May 15, 2020
OBJECTIVE: To determine if natural language processing (NLP) improves detection of nonsevere hypoglycemia (NSH) in patients with type 2 diabetes and no NSH documentation by diagnosis codes and to measure if NLP detection improves the prediction of fu...
Male Middle Aged Severity of Illness Index Diabetes Mellitus, Type 2 United States Aged Aged, 80 and over Young Adult Adult Community Health Planning Clinical Decision Rules Predictive Value of Tests Natural Language Processing Hypoglycemia Information Storage and Retrieval International Classification of Diseases Humans Algorithms Electronic Health Records Female
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Automated detection and classification of diabetes disease based on Bangladesh demographic and health survey data, 2011 using machine learning approach.

Diabetes & metabolic syndrome Mar 10, 2020
BACKGROUND AND AIMS: Diabetes has been recognized as a continuing health challenge for the twenty-first century, both in developed and developing countries including Bangladesh. The main objective of this study is to use machine learning (ML) based c...
Logistic Models Treatment Outcome Diabetes Mellitus Support Vector Machine Factor Analysis, Statistical Area Under Curve Discriminant Analysis Automation Bangladesh Demography Adult Male Middle Aged Health Surveys Humans Diabetes Mellitus, Type 2 Machine Learning Female Reproducibility of Results
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A health informatics transformation model based on intelligent cloud computing - exemplified by type 2 diabetes mellitus with related cardiovascular diseases.

Computer methods and programs in biomedicine Feb 25, 2020
BACKGROUND AND OBJECTIVE: Many studies regarding health analysis request structured datasets but the legacy resources provide scattered data. This study aims to establish a health informatics transformation model (HITM) based upon intelligent cloud c...
Medical Informatics Cloud Computing Humans Machine Learning Artificial Intelligence Algorithms Cardiovascular Diseases Diabetes Mellitus, Type 2
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An Artificial Neural Network-based Predictive Model to Support Optimization of Inpatient Glycemic Control.

Diabetes technology & therapeutics Feb 6, 2020
Achieving glycemic control in critical care patients is of paramount importance, and has been linked to reductions in mortality, intensive care unit (ICU) length of stay, and morbidities such as infection. The myriad of illnesses and patient conditi...
Humans Neural Networks, Computer Female Diabetes Mellitus, Type 2 Hypoglycemic Agents Aged Inpatients Male Blood Glucose Self-Monitoring Middle Aged Glycemic Control Blood Glucose Insulin
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Predicting 10-Year Risk of End-Organ Complications of Type 2 Diabetes With and Without Metabolic Surgery: A Machine Learning Approach.

Diabetes care Feb 6, 2020
OBJECTIVE: To construct and internally validate prediction models to estimate the risk of long-term end-organ complications and mortality in patients with type 2 diabetes and obesity that can be used to inform treatment decisions for patients and pra...
Diabetes Complications Adult Male Middle Aged Disease Progression Retrospective Studies Adolescent Humans Time Factors Female Machine Learning Diabetes Mellitus, Type 2 Aged Computer Simulation Follow-Up Studies Aged, 80 and over Prognosis Bariatric Surgery Young Adult Organs at Risk
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The feature extraction of resting-state EEG signal from amnestic mild cognitive impairment with type 2 diabetes mellitus based on feature-fusion multispectral image method.

Neural networks : the official journal of the International Neural Network Society Jan 30, 2020
Recently, combining feature extraction and classification method of electroencephalogram (EEG) signals has been widely used in identifying mild cognitive impairment. However, it remains unclear which feature of EEG signals is best effective in assess...
Electroencephalography Humans Neural Networks, Computer Cognitive Dysfunction Diabetes Mellitus, Type 2
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Deep learning models predict regulatory variants in pancreatic islets and refine type 2 diabetes association signals.

eLife Jan 27, 2020
Genome-wide association analyses have uncovered multiple genomic regions associated with T2D, but identification of the causal variants at these remains a challenge. There is growing interest in the potential of deep learning models - which predict e...
Genetic Predisposition to Disease Models, Theoretical Polymorphism, Single Nucleotide Chromatin Humans Epigenomics Islets of Langerhans Deep Learning Signal Transduction Genome-Wide Association Study Diabetes Mellitus, Type 2
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