Diabetes

[Stage Ⅳ Breast Cancer with Difficulty in Initiating Chemotherapy-A Case Report].

We report a case of breast cancer(T4b[skin], N1, M1[lung], ER-, PR-, HER2 3+)in a 63-year-old woman with liver dysfunction of unknown cause(T-Bil 3.6mg/dL, ALP 3,483 U/L, AST 214 U/L, ALT 320 U/L, g / -GTP 1,943 U/L). Further- more, serum CA19-9(4,670 U/mL)and HbA1c(8.8%)levels were both elevated. First, she underwent chemotherapy with trastuzumab and capecitabine. Subsequently, liver dysfunction ...

Deep Transfer Learning Models for Medical Diabetic Retinopathy Detection.

INTRODUCTION: Diabetic retinopathy (DR) is the most common diabetic eye disease worldwide and a leading cause of blindness. The number of diabetic patients will increase to 552 million by 2034, as per the International Diabetes Federation (IDF).

Relevant Features in Nonalcoholic Steatohepatitis Determined Using Machine Learning for Feature Selection.

We investigated the prevalence and the most relevant features of nonalcoholic steatohepatitis (NASH), a stage of nonalcoholic fatty liver disease, (N...

An FP's guide to AI-enabled clinical decision support.

To better understand the capabilities and challenges of artificial intelligence and machine learning, we look at the role they can play in screening f...

Ex-vivo antihypertensive and calcium channel blocking activity of Androsace foliosa n-hexane leaves fraction on isolated rabbit aorta.

Hypertension is persistent elevation in blood pressure for 3-4 weeks. Estimated global prevalence of hypertension suggested that by the Year 2025 (29%...

Convolutional neural networks for wound detection: the role of artificial intelligence in wound care.

OBJECTIVE: Telemedicine is an essential support system for clinical settings outside the hospital. Recently, the importance of the model for assessmen...

A new and efficient numerical method for the fractional modeling and optimal control of diabetes and tuberculosis co-existence.

The main objective of this research is to investigate a new fractional mathematical model involving a nonsingular derivative operator to discuss the c...

Identifying Diabetes in Clinical Notes in Hebrew: A Novel Text Classification Approach Based on Word Embedding.

NimbleMiner is a word embedding-based, language-agnostic natural language processing system for clinical text classification. Previously, NimbleMiner ...

Artificial intelligence in diabetic retinopathy: A natural step to the future.

Use of artificial intelligence in medicine in an evolving technology which holds promise for mass screening and perhaps may even help in establishing ...

An Interpretable Ensemble Deep Learning Model for Diabetic Retinopathy Disease Classification.

Diabetic retinopathy (DR) is one kind of eye disease that is caused by overtime diabetes. Lots of patients around the world suffered from DR which may...

Homogeneous and heterogeneous ensemble classification methods in diabetes disease: a review.

This paper explores the use of ensemble classification methods in the context of the diabetes disease. An analysis was carried out that formulates and...

Automatic Parallel Detection of Neovascularization from Retinal Images Using Ensemble of Extreme Learning Machine.

Retinopathy screening is a non-invasive method to collect retinal images and neovascularization detection from retinal images plays a significant role...

An outcome model approach to transporting a randomized controlled trial results to a target population.

OBJECTIVE: Participants enrolled into randomized controlled trials (RCTs) often do not reflect real-world populations. Previous research in how best t...

Using Machine Learning Applied to Real-World Healthcare Data for Predictive Analytics: An Applied Example in Bariatric Surgery.

OBJECTIVES: Laparoscopic metabolic surgery (MxS) can lead to remission of type 2 diabetes (T2D); however, treatment response to MxS can be heterogeneo...

Assessment of Deep Generative Models for High-Resolution Synthetic Retinal Image Generation of Age-Related Macular Degeneration.

IMPORTANCE: Deep learning (DL) used for discriminative tasks in ophthalmology, such as diagnosing diabetic retinopathy or age-related macular degenera...

Deep Learning Applications in Chest Radiography and Computed Tomography: Current State of the Art.

Deep learning is a genre of machine learning that allows computational models to learn representations of data with multiple levels of abstraction usi...

Deep Learning Predicts OCT Measures of Diabetic Macular Thickening From Color Fundus Photographs.

PURPOSE: To develop deep learning (DL) models for the automatic detection of optical coherence tomography (OCT) measures of diabetic macular thickenin...

Continuous Glucose Monitoring Linked to an Artificial Intelligence Risk Index: Early Footprints of Intraventricular Hemorrhage in Preterm Neonates.

OBJECTIVE: To develop and validate a new risk score for intraventricular hemorrhage (IVH) in preterm neonates based on continuous glucose monitoring (...

Machine Learning for the Prediction of New-Onset Diabetes Mellitus during 5-Year Follow-up in Non-Diabetic Patients with Cardiovascular Risks.

PURPOSE: Many studies have proposed predictive models for type 2 diabetes mellitus (T2DM). However, these predictive models have several limitations, ...

Fuzzy logic based risk assessment system giving individualized advice for metabolic syndrome and fatal cardiovascular diseases.

In 2005, global cardiovascular diseases caused 30% of deaths in Europe, which is 46% of total deaths for all death groups. Today, according to the Int...