Primary Care

Obesity

Latest AI and machine learning research in obesity for healthcare professionals.

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Factors Affecting Transperitoneal Robot-Assisted Laparoscopic Radical Prostatectomy.

To evaluate the impact of body mass index (BMI), preoperative risk classification, previous inguina...

Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare.

Chronic diseases are a growing concern worldwide, with nearly 25% of adults suffering from one or mo...

Risk Assessment and Determination of Factors That Cause the Development of Hyperinsulinemia in School-Age Adolescents.

: Hyperinsulinemia and insulin resistance are not synonymous; if the risk of developing insulin resi...

Heart Rate Modeling and Prediction Using Autoregressive Models and Deep Learning.

Physiological time series are affected by many factors, making them highly nonlinear and nonstationa...

Proof of concept and development of a couple-based machine learning model to stratify infertile patients with idiopathic infertility.

We aimed to develop and evaluate a machine learning model that can stratify infertile/fertile couple...

A stratified analysis of a deep learning algorithm in the diagnosis of diabetic retinopathy in a real-world study.

BACKGROUND: The aim of our research was to prospectively explore the clinical value of a deep learni...

Unsupervised Learning for Automated Detection of Coronary Artery Disease Subgroups.

Background The promise of precision population health includes the ability to use robust patient dat...

An interpretable machine learning model based on a quick pre-screening system enables accurate deterioration risk prediction for COVID-19.

A high-performing interpretable model is proposed to predict the risk of deterioration in coronaviru...

A Comparison among Different Machine Learning Pretest Approaches to Predict Stress-Induced Ischemia at PET/CT Myocardial Perfusion Imaging.

Traditional approach for predicting coronary artery disease (CAD) is based on demographic data, symp...

Combining machine learning and conventional statistical approaches for risk factor discovery in a large cohort study.

We present a simple and efficient hypothesis-free machine learning pipeline for risk factor discover...

Network analysis of trauma in patients with early-stage psychosis.

Childhood trauma (ChT) is a risk factor for psychosis. Negative lifestyle factors such as rumination...

Don't Overweight Weights: Evaluation of Weighting Strategies for Multi-Task Bioactivity Classification Models.

Machine learning models predicting the bioactivity of chemical compounds belong nowadays to the stan...

Emulating complex simulations by machine learning methods.

BACKGROUND: The aim of the present paper is to construct an emulator of a complex biological system ...

A Machine Learning Approach to Predictive Modelling of Student Performance.

- Many factors affect student performance such as the individual's background, habits, absenteeism ...

Combining novel technologies with interdisciplinary basic research to enhance horticultural crops.

Horticultural crops mainly include fruits, vegetables, ornamental trees and flowers, and tea trees (...

Detection of diabetes from whole-body MRI using deep learning.

Obesity is one of the main drivers of type 2 diabetes, but it is not uniformly associated with the d...

Obesity Mass Monitoring in Medical Big Data Based on High-Order Simulated Annealing Neural Network Algorithm.

With the rapid development of information technology, hospital informatization has become the genera...

Multifactor Prediction of Embryo Transfer Outcomes Based on a Machine Learning Algorithm.

fertilization-embryo transfer (IVF-ET) technology make it possible for infertile couples to conceiv...

Using CatBoost algorithm to identify middle-aged and elderly depression, national health and nutrition examination survey 2011-2018.

Depression is one of the most common mental health problems in middle-aged and elderly people. The e...

Neural interface systems with on-device computing: machine learning and neuromorphic architectures.

Development of neural interface and brain-machine interface (BMI) systems enables the treatment of n...

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