Primary Care

Diabetes disease detection and classification on Indian demographic and health survey data using machine learning methods.

BACKGROUND & AIM: Diabetes mellitus has become one of the out brakes causing major health issues in developing countries like India. The need for leveraging technology is felt in diabetes management. The main objective of this work is to deploy machine learning methods for the detection and classification of diabetes having clinical relevance.

Analyzing factors contributing to COVID-19 mortality in the United States using artificial intelligence techniques.

Having started since late 2019, COVID-19 has spread through far many nations around the globe. Not being known profoundly, the novel virus of the Coronaviruses family has already caused more than half a million deaths and put the lives of many more people in danger. Policymakers have implemented preventive measures to curb the outbreak of the virus, and health practitioners along with epidemiologi...

Artificial intelligence model with deep learning in nonalcoholic fatty liver disease diagnosis: genetic based artificial neural networks.

Nonalcoholic fatty liver disease (NAFLD) is one of the most common causes of chronic liver disease in the world. The NAFLD spectrum includes simple st...

Aortic Distensibility Measured by Automated Analysis of Magnetic Resonance Imaging Predicts Adverse Cardiovascular Events in UK Biobank.

Background Automated analysis of cardiovascular magnetic resonance images provides the potential to assess aortic distensibility in large populations....

Development and validation of a machine learning-augmented algorithm for diabetes screening in community and primary care settings: A population-based study.

BACKGROUND: Opportunely screening for diabetes is crucial to reduce its related morbidity, mortality, and socioeconomic burden. Machine learning (ML) ...

Integrative transcriptome analysis of SARS-CoV-2 human-infected cells combined with deep learning algorithms identifies two potential cellular targets for the treatment of coronavirus disease.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) quickly spread worldwide, leading coronavirus disease 2019 (COVID-19) to hit pandemic lev...

Determination of Wheat Heading Stage Using Convolutional Neural Networks on Multispectral UAV Imaging Data.

The heading and flowering stages are crucial for wheat growth and should be used for fusarium head blight (FHB) and other plant prevention operations....

Using artificial intelligence to optimize delivery of weight loss treatment: Protocol for an efficacy and cost-effectiveness trial.

Gold standard behavioral weight loss (BWL) is limited by the availability of expert clinicians and high cost of delivery. The artificial intelligence ...

Systematic review of academic robotic surgery curricula.

The use of robotic surgery has increased exponentially in the United States. Despite this uptick in popularity, no standardized training pathway exist...

Identifying an Optimal Neuroinflammation Treatment Using a Nanoligomer Discovery Engine.

Acute activation of innate immune response in the brain, or neuroinflammation, protects this vital organ from a range of external pathogens and promot...

Classification and Prediction on Hypertension with Blood Pressure Determinants in a Deep Learning Algorithm.

Few studies classified and predicted hypertension using blood pressure (BP)-related determinants in a deep learning algorithm. The objective of this s...

Deffini: A family-specific deep neural network model for structure-based virtual screening.

Deep learning-based virtual screening methods have been shown to significantly improve the accuracy of traditional docking-based virtual screening met...

A correlation-based feature analysis of physical examination indicators can help predict the overall underlying health status using machine learning.

As a systematic investigation of the correlations between physical examination indicators (PEIs) is lacking, most PEIs are currently independently use...

Machine Learning Models for Data-Driven Prediction of Diabetes by Lifestyle Type.

The prevalence of diabetes has been increasing in recent years, and previous research has found that machine-learning models are good diabetes predict...

Expressions of serum adiponectin and visfatin in patients with hypertension in cerebrovascular accidents and analysis of risk factors.

OBJECTIVE: To determine the expressions of serum adiponectin and visfatin in patients with hypertension and cerebrovascular accidents and to analyze t...

Design and Usability of an Avatar-Based Learning Program to Support Diabetes Education: Quality Improvement Study in Colombia.

BACKGROUND: This quality improvement study, entitled Avatar-Based LEarning for Diabetes Optimal Control (ABLEDOC), explored the feasibility of deliver...

Causal deep learning reveals the comparative effectiveness of antihyperglycemic treatments in poorly controlled diabetes.

Type-2 diabetes is associated with severe health outcomes, the effects of which are responsible for approximately 1/4 of the total healthcare spending...

Robot-assisted duodenal switch with DaVinci Xi: surgical technique and analysis of a single-institution experience of 661 cases.

Metabolic and bariatric surgery is an effective treatment for the management of obesity and related comorbidities. Although the duodenal switch has de...

NAVIGATOR: an Italian regional imaging biobank to promote precision medicine for oncologic patients.

NAVIGATOR is an Italian regional project boosting precision medicine in oncology with the aim of making it more predictive, preventive, and personalis...

MILCDock: Machine Learning Enhanced Consensus Docking for Virtual Screening in Drug Discovery.

Molecular docking tools are regularly used to computationally identify new molecules in virtual screening for drug discovery. However, docking tools s...