AIMC Topic: Diabetes Complications
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A Novel Approach for Continuous Health Status Monitoring and Automatic Detection of Infection Incidences in People With Type 1 Diabetes Using Machine Learning Algorithms (Part 2): A Personalized Digital Infectious Disease Detection Mechanism.
Journal of medical Internet research
Aug 12, 2020
BACKGROUND: Semisupervised and unsupervised anomaly detection methods have been widely used in various applications to detect anomalous objects from a given data set. Specifically, these methods are popular in the medical domain because of their suit...
Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts.
PLoS medicine
Jun 19, 2020
BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible dama...
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...
Towards more Accessible Precision Medicine: Building a more Transferable Machine Learning Model to Support Prognostic Decisions for Micro- and Macrovascular Complications of Type 2 Diabetes Mellitus.
Journal of medical systems
May 17, 2019
Although machine learning models are increasingly being developed for clinical decision support for patients with type 2 diabetes, the adoption of these models into clinical practice remains limited. Currently, machine learning (ML) models are being ...
Deep learning strategy for accurate carotid intima-media thickness measurement: An ultrasound study on Japanese diabetic cohort.
Computers in biology and medicine
May 12, 2018
MOTIVATION: The carotid intima-media thickness (cIMT) is an important biomarker for cardiovascular diseases and stroke monitoring. This study presents an intelligence-based, novel, robust, and clinically-strong strategy that uses a combination of dee...
Tuberculosis diagnosis support analysis for precarious health information systems.
Computer methods and programs in biomedicine
Jan 11, 2018
BACKGROUND AND OBJECTIVE: Pulmonary tuberculosis is a world emergency for the World Health Organization. Techniques and new diagnosis tools are important to battle this bacterial infection. There have been many advances in all those fields, but in de...
Predictions of ocular changes caused by diabetes in glaucoma patients.
Computer methods and programs in biomedicine
Nov 15, 2017
BACKGROUND AND OBJECTIVE: This paper builds different neural network models with simple topologies, having one or two hidden layers which were subsequently employed in the prediction of ocular changes progression in patients with diabetes associated ...
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 ...
Confirming an integrated pathology of diabetes and its complications by molecular biomarker-target network analysis.
Molecular medicine reports
Jul 7, 2016
Despite ongoing research into diabetes and its complications, the underlying molecular associations remain to be elucidated. The systematic identification of molecular interactions in associated diseases may be approached using a network analysis str...
Serum cystatin C and neutrophil gelatinase-associated lipocalin in predicting the severity of coronary artery disease in diabetic patients.
Anatolian journal of cardiology
Nov 26, 2015
OBJECTIVE: Cystatin C and neutrophil gelatinase-associated lipocalin (NGAL) are biomarkers of renal functions. We evaluated their roles in predicting the severity of coronary artery disease (CAD).
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