Primary Care

Obesity

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

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Predictive model for abdominal liposuction volume in patients with obesity using machine learning in a longitudinal multi-center study in Korea.

This study aimed to develop and validate a machine learning (ML)-based model for predicting liposuct...

Predicting early mortality in hemodialysis patients: a deep learning approach using a nationwide prospective cohort in South Korea.

Early mortality after hemodialysis (HD) initiation significantly impacts the longevity of HD patient...

Integrating Remote Photoplethysmography and Machine Learning on Multimodal Dataset for Noninvasive Heart Rate Monitoring.

Non-contact heart monitoring is crucial in advancing telemedicine, fitness tracking, and mass screen...

A hybrid healthy diet recommender system based on machine learning techniques.

Obesity is a chronic disease correlated with numerous risk factors that not only negatively affect a...

Machine Learning Algorithms Exceed Comorbidity Indices in Prediction of Short-Term Complications After Hip Fracture Surgery.

BACKGROUND: Hip fractures are among the most morbid acute orthopaedic injuries often due to accompan...

Explainable Deep Learning Approaches for Risk Screening of Periodontitis.

Several pieces of evidence have been reported regarding the association between periodontitis and sy...

Application of Machine Learning and Deep Neural Visual Features for Predicting Adult Obesity Prevalence in Missouri.

This research study investigates and predicts the obesity prevalence in Missouri, utilizing deep neu...

Inferring the locomotor ecology of two of the oldest fossil squirrels: influence of operationalization, trait, body size and machine learning method.

Correlations between morphology and lifestyle of extant taxa are useful for predicting lifestyles of...

The Role of Artificial Intelligence in Obesity Medicine.

The rising prevalence of obesity presents significant health, economic, and social challenges, neces...

Predicting frailty in older patients with chronic pain using explainable machine learning: A cross-sectional study.

Frailty is common among older adults with chronic pain, and early identification is crucial in preve...

Development and external validation of an interpretable machine learning model for the prediction of intubation in the intensive care unit.

Given the limited capacity to accurately determine the necessity for intubation in intensive care un...

Decoding multi-limb movements from two-photon calcium imaging of neuronal activity using deep learning.

Brain-machine interfaces (BMIs) aim to restore sensorimotor function to individuals suffering from n...

Comparison between the EKFC-equation and machine learning models to predict Glomerular Filtration Rate.

In clinical practice, the glomerular filtration rate (GFR), a measurement of kidney functioning, is ...

Development and validation of a machine learning model for predicting drug-drug interactions with oral diabetes medications.

Diabetes management is often complicated by comorbidities, requiring complex medication regimens tha...

Identification of metabolism related biomarkers in obesity based on adipose bioinformatics and machine learning.

BACKGROUND: Obesity has emerged as a growing global public health concern over recent decades. Obesi...

Multi-Activity Step Counting Algorithm Using Deep Learning Foot Flat Detection with an IMU Inside the Sole of a Shoe.

Step counting devices were previously shown to be efficient in a variety of applications such as ath...

Machine Learning Models for Predicting Significant Liver Fibrosis in Patients with Severe Obesity and Nonalcoholic Fatty Liver Disease.

PURPOSE: Although noninvasive tests can be used to predict liver fibrosis, their accuracy is limited...

Predicting non-responders to lifestyle intervention in prediabetes: a machine learning approach.

BACKGROUND: The clinical care process for people with prediabetes starts with lifestyle intervention...

Diagnostic performance of single-lead electrocardiograms for arterial hypertension diagnosis: a machine learning approach.

Awareness and early identification of hypertension is crucial in reducing the burden of cardiovascul...

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