AIMC Topic: Diabetes Mellitus, Type 2
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Learning ensemble classifiers for diabetic retinopathy assessment.
Artificial intelligence in medicine
Oct 6, 2017
Diabetic retinopathy is one of the most common comorbidities of diabetes. Unfortunately, the recommended annual screening of the eye fundus of diabetic patients is too resource-consuming. Therefore, it is necessary to develop tools that may help doct...
Electronic Health Records
Clinical Decision-Making
Reproducibility of Results
Risk Factors
Diabetic Retinopathy
Machine Learning
Decision Support Techniques
Diabetes Mellitus, Type 1
Prognosis
Risk Assessment
Fuzzy Logic
Decision Trees
Predictive Value of Tests
Humans
Time Factors
Decision Support Systems, Clinical
Diabetes Mellitus, Type 2
Single Nucleotide Polymorphism relevance learning with Random Forests for Type 2 diabetes risk prediction.
Artificial intelligence in medicine
Sep 22, 2017
OBJECTIVE: The use of artificial intelligence techniques to find out which Single Nucleotide Polymorphisms (SNPs) promote the development of a disease is one of the features of medical research, as such techniques may potentially aid early diagnosis ...
Defining and characterizing the critical transition state prior to the type 2 diabetes disease.
PloS one
Jul 7, 2017
BACKGROUND: Type 2 diabetes mellitus (T2DM), with increased risk of serious long-term complications, currently represents 8.3% of the adult population. We hypothesized that a critical transition state prior to the new onset T2DM can be revealed throu...
Machine Learning Methods to Predict Diabetes Complications.
Journal of diabetes science and technology
May 12, 2017
One of the areas where Artificial Intelligence is having more impact is machine learning, which develops algorithms able to learn patterns and decision rules from data. Machine learning algorithms have been embedded into data mining pipelines, which ...
Cardiovascular events in patients with mild autonomous cortisol secretion: analysis with artificial neural networks.
European journal of endocrinology
May 3, 2017
BACKGROUND: The independent role of mild autonomous cortisol secretion (ACS) in influencing the cardiovascular event (CVE) occurrence is a topic of interest. We investigated the role of mild ACS in the CVE occurrence in patients with adrenal incident...
Learning Effective Treatment Pathways for Type-2 Diabetes from a clinical data warehouse.
AMIA ... Annual Symposium proceedings. AMIA Symposium
Feb 10, 2017
Treatment guidelines for management of type-2 diabetes mellitus (T2DM) are controversial because existing evidence from randomized clinical trials do not address many important clinical questions. Data from Electronic Medical Records (EMRs) has been ...
Predicting DPP-IV inhibitors with machine learning approaches.
Journal of computer-aided molecular design
Feb 2, 2017
Dipeptidyl peptidase IV (DPP-IV) is a promising Type 2 diabetes mellitus (T2DM) drug target. DPP-IV inhibitors prolong the action of glucagon-like peptide-1 (GLP-1) and gastric inhibitory peptide (GIP), improve glucose homeostasis without weight gain...
Carotid Intima-Media Thickness and Visit-to-Visit HbA1c Variability Predict Progression of Chronic Kidney Disease in Type 2 Diabetic Patients with Preserved Kidney Function.
Journal of diabetes research
Dec 20, 2016
. Subclinical atherosclerosis and long-term glycemic variability have been reported to predict incident chronic kidney disease (CKD) in the general population. However, these associations have not been investigated in patients with type 2 diabetes wi...
Development of Type 2 Diabetes Mellitus Phenotyping Framework Using Expert Knowledge and Machine Learning Approach.
Journal of diabetes science and technology
Dec 7, 2016
BACKGROUND: Phenotyping is an automated technique that can be used to distinguish patients based on electronic health records. To improve the quality of medical care and advance type 2 diabetes mellitus (T2DM) research, the demand for T2DM phenotypin...
An Intelligible Risk Stratification Model Based on Pairwise and Size Constrained Kmeans.
IEEE journal of biomedical and health informatics
Nov 29, 2016
Having a system to stratify individuals according to risk is key to clinical disease prevention. This allows individuals identified at different risk tiers to benefit from further investigation and intervention. But the same risk score estimated for ...
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