Primary Care

Obesity

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

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Estimated Artificial Neural Network Modeling of Maximal Oxygen Uptake Based on Multistage 10-m Shuttle Run Test in Healthy Adults.

We aimed to develop an artificial neural network (ANN) model to estimate the maximal oxygen uptake (...

Machine learning using clinical data at baseline predicts the efficacy of vedolizumab at week 22 in patients with ulcerative colitis.

Predicting the response of patients with ulcerative colitis (UC) to a biologic such as vedolizumab (...

Human activity recognition using wearable sensors, discriminant analysis, and long short-term memory-based neural structured learning.

Healthcare using body sensor data has been getting huge research attentions by a wide range of resea...

Natural language processing for the assessment of cardiovascular disease comorbidities: The cardio-Canary comorbidity project.

OBJECTIVE: Accurate ascertainment of comorbidities is paramount in clinical research. While manual a...

Robotic versus laparoscopic right hemicolectomy: a case-matched study.

The current gold standard surgical treatment for right colonic malignancy is the laparoscopic right ...

CAFT: a deep learning-based comprehensive abdominal fat analysis tool for large cohort studies.

BACKGROUND: There is increasing appreciation of the association of obesity beyond co-morbidities, su...

Data-driven identification of complex disease phenotypes.

Disease interaction in multimorbid patients is relevant to treatment and prognosis, yet poorly under...

Future of machine learning in paediatrics.

Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to learn wi...

PEDF, a pleiotropic WTC-LI biomarker: Machine learning biomarker identification and validation.

Biomarkers predict World Trade Center-Lung Injury (WTC-LI); however, there remains unaddressed multi...

Support Vector Machine as a Supervised Learning for the Prioritization of Novel Potential SARS-CoV-2 Main Protease Inhibitors.

In the last year, the COVID-19 pandemic has highly affected the lifestyle of the world population, e...

Using Wearables and Machine Learning to Enable Personalized Lifestyle Recommendations to Improve Blood Pressure.

Blood pressure (BP) is an essential indicator for human health and is known to be greatly influence...

Machine Learning to Identify Metabolic Subtypes of Obesity: A Multi-Center Study.

BACKGROUND AND OBJECTIVE: Clinical characteristics of obesity are heterogenous, but current classifi...

Inter-laboratory automation of the in vitro micronucleus assay using imaging flow cytometry and deep learning.

The in vitro micronucleus assay is a globally significant method for DNA damage quantification used ...

Machine Learning for Predicting the 3-Year Risk of Incident Diabetes in Chinese Adults.

We aimed to establish and validate a risk assessment system that combines demographic and clinical ...

Severe intraoperative bleeding predicts the risk of perioperative blood transfusion after robot-assisted radical prostatectomy.

To evaluate potential factors associated with the risk of perioperative blood transfusion (PBT) with...

Optimizing Robotic Hysterectomy for the Patient Who Is Morbidly Obese with a Surgical Safety Pathway.

STUDY OBJECTIVE: Obesity is a growing worldwide epidemic, and patients classified as obese undergoin...

Robot-assisted kidney transplantation is a safe alternative approach for morbidly obese patients with end-stage renal disease.

BACKGROUND: Many centres deny obese patients with a body mass index (BMI) >35 access to kidney trans...

Personalized machine learning of depressed mood using wearables.

Depression is a multifaceted illness with large interindividual variability in clinical response to ...

Design Comorbidity Portfolios to Improve Treatment Cost Prediction of Asthma Using Machine Learning.

Comorbidity is an important factor to consider when trying to predict the cost of treating asthma pa...

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