Obesity

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

BACKGROUND AND OBJECTIVE: Clinical characteristics of obesity are heterogenous, but current classification for diagnosis is simply based on BMI or metabolic healthiness. The purpose of this study was to use machine learning to explore a more precise classification of obesity subgroups towards informing individualized therapy.

Health Recognition Algorithm for Sports Training Based on Bi-GRU Neural Networks.

The healthcare benefits associated with regular physical activity recognition and monitoring have been considered in several research studies. Regular recognition and monitoring of health status can potentially assist in managing and reducing the risk of many diseases such as cardiovascular disease, diabetes, and obesity. Using healthcare equipment in hospitals, people can conduct regular physical...

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 for regulatory compound safety testing in addition...

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 variables to predict the 3-year risk of incident d...

Discovery of novel DGAT1 inhibitors by combination of machine learning methods, pharmacophore model and 3D-QSAR model.

DGAT1 plays a crucial controlling role in triglyceride biosynthetic pathways, which makes it an attractive therapeutic target for obesity. Thus, devel...

Contrasting factors associated with COVID-19-related ICU admission and death outcomes in hospitalised patients by means of Shapley values.

Identification of those at greatest risk of death due to the substantial threat of COVID-19 can benefit from novel approaches to epidemiology that lev...

Prediction of drug efficacy from transcriptional profiles with deep learning.

Drug discovery focused on target proteins has been a successful strategy, but many diseases and biological processes lack obvious targets to enable su...

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 implications on length of hospital stay (LOHS) an...

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 undergoing gynecologic robotic surgery are at increased ris...

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 transplantation due to increased intraoperative and pos...

Personalized machine learning of depressed mood using wearables.

Depression is a multifaceted illness with large interindividual variability in clinical response to treatment. In the era of digital medicine and prec...

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 patients. When an asthmatic patient suffered from co...

Predicting anxiety in cancer survivors presenting to primary care - A machine learning approach accounting for physical comorbidity.

BACKGROUND: The purpose of this study was to explore predictors for anxiety as the most common form of psychological distress in cancer survivors whil...

Improving Stroke Risk Prediction in the General Population: A Comparative Assessment of Common Clinical Rules, a New Multimorbid Index, and Machine-Learning-Based Algorithms.

BACKGROUND: There are few large studies examining and predicting the diversified cardiovascular/noncardiovascular comorbidity relationships with strok...

Robot-assisted sleeve gastrectomy in patients with obesity with a novel Chinese domestic MicroHand SII surgical system.

BACKGROUND: A new device has been added to the Chinese MicroHand surgical robot family, developed based on the successful application of control algor...

Prediction of Bedridden Duration of Hospitalized Patients by Machine Learning Based on EMRs at Admission.

Being bedridden is a frequent comorbid condition that leads to a series of complications in clinical practice. The present study aimed to predict bedr...

An study of lipase inhibitory peptides obtained from de-oiled rice bran.

De-oiled rice bran (DORB) is a potentially useful by-product of the rice bran oil industry. DORB may prove to be an important protein source, and also...

Classification and Prediction on the Effects of Nutritional Intake on Overweight/Obesity, Dyslipidemia, Hypertension and Type 2 Diabetes Mellitus Using Deep Learning Model: 4-7th Korea National Health and Nutrition Examination Survey.

Few studies have been conducted to classify and predict the influence of nutritional intake on overweight/obesity, dyslipidemia, hypertension and type...

A real-time anatomy ıdentification via tool based on artificial ıntelligence for ultrasound-guided peripheral nerve block procedures: an accuracy study.

We aimed to assess the accuracy of an artificial intelligence (AI)-based real-time anatomy identification software specifically developed to ease imag...

Integration of NLP2FHIR Representation with Deep Learning Models for EHR Phenotyping: A Pilot Study on Obesity Datasets.

HL7 Fast Healthcare Interoperability Resources (FHIR) is one of the current data standards for enabling electronic healthcare information exchange. Pr...