Diabetes

Estimating individualized optimal combination therapies through outcome weighted deep learning algorithms.

With the advancement in drug development, multiple treatments are available for a single disease. Patients can often benefit from taking multiple treatments simultaneously. For example, patients in Clinical Practice Research Datalink with chronic diseases such as type 2 diabetes can receive multiple treatments simultaneously. Therefore, it is important to estimate what combination therapy from whi...

Machine learning algorithm-based risk prediction model of coronary artery disease.

In view of high mortality associated with coronary artery disease (CAD), development of an early predicting tool will be beneficial in reducing the burden of the disease. The database comprising demographic, conventional, folate/xenobiotic genetic risk factors of 648 subjects (364 cases of CAD and 284 healthy controls) was used as the basis to develop CAD risk and percentage stenosis prediction mo...

Machine Learning Helps Identify New Drug Mechanisms in Triple-Negative Breast Cancer.

This paper demonstrates the ability of mach- ine learning approaches to identify a few genes among the 23,398 genes of the human genome to experiment ...

Ontology-based literature mining and class effect analysis of adverse drug reactions associated with neuropathy-inducing drugs.

BACKGROUND: Adverse drug reactions (ADRs), also called as drug adverse events (AEs), are reported in the FDA drug labels; however, it is a big challen...

Plasminogen activator inhibitor-1 is associated with the metabolism and development of advanced colonic polyps.

Implications of plasminogen activator inhibitor-1 (PAI-1) in colonic polyps remain elusive. A prospective study was conducted with 188 consecutive sub...

Deep learning in ophthalmology: a review.

Deep learning is an emerging technology with numerous potential applications in Ophthalmology. Deep learning tools have been applied to different diag...

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review.

BACKGROUND: Artificial intelligence methods in combination with the latest technologies, including medical devices, mobile computing, and sensor techn...

Microaneurysm Detection Using Principal Component Analysis and Machine Learning Methods.

Diabetic retinopathy (DR) is an eye abnormality caused by long-term diabetes and it is the most common cause of blindness before the age of 50. Microa...

Structure-Preserving Guided Retinal Image Filtering and Its Application for Optic Disk Analysis.

Retinal fundus photographs have been used in the diagnosis of many ocular diseases such as glaucoma, pathological myopia, age-related macular degenera...

Factors associated with dementia in elderly.

We analyzed the factors associated with dementia in the elderly attended at a memory outpatient clinic of the University of Southern Santa Catarina (U...

Phrase mining of textual data to analyze extracellular matrix protein patterns across cardiovascular disease.

Extracellular matrix (ECM) proteins have been shown to play important roles regulating multiple biological processes in an array of organ systems, inc...

Thread/paper- and paper-based microfluidic devices for glucose assays employing artificial neural networks.

This paper describes the fabrication of and data collection from two microfluidic devices: a microfluidic thread/paper based analytical device (μTPAD)...

Deep learning strategy for accurate carotid intima-media thickness measurement: An ultrasound study on Japanese diabetic cohort.

MOTIVATION: The carotid intima-media thickness (cIMT) is an important biomarker for cardiovascular diseases and stroke monitoring. This study presents...

Extracting Healthcare Quality Information from Unstructured Data.

Healthcare quality research is a fundamental task that involves assessing treatment patterns and measuring the associated patient outcomes to identify...

The Problems of Realism-Based Ontology Design: a Case Study in Creating Definitions for an Application Ontology for Diabetes Camps.

A requirement of realism-based ontology design is that classes denote exclusively entities that exist objectively in reality and that their definition...

Leveraging existing corpora for de-identification of psychiatric notes using domain adaptation.

De-identification of clinical notes is a special case of named entity recognition. Supervised machine-learning (ML) algorithms have achieved promising...

Utilizing Smartphone-Based Machine Learning in Medical Monitor Data Collection: Seven Segment Digit Recognition.

Biometric measurements captured from medical devices, such as blood pressure gauges, glucose monitors, and weighing scales, are essential to tracking ...

Long-term outcome in inherited nephrogenic diabetes insipidus.

BACKGROUND: Inherited nephrogenic diabetes insipidus (NDI) is a rare disorder characterized by impaired urinary concentrating ability. Little clinical...

Antiproliferative and anti-apoptotic effect of astaxanthin in an oxygen-induced retinopathy mouse model.

OBJECTIVE: To evaluate the impact of intravitreal (IV) and intraperitoneal (IP) astaxanthin (AST) injections on neovascular development (ND), retinal ...

Accurate Diabetes Risk Stratification Using Machine Learning: Role of Missing Value and Outliers.

Diabetes mellitus is a group of metabolic diseases in which blood sugar levels are too high. About 8.8% of the world was diabetic in 2017. It is proje...