Obesity

A machine learning approach to identify distinct subgroups of veterans at risk for hospitalization or death using administrative and electronic health record data.

BACKGROUND: Identifying individuals at risk for future hospitalization or death has been a major priority of population health management strategies. High-risk individuals are a heterogeneous group, and existing studies describing heterogeneity in high-risk individuals have been limited by data focused on clinical comorbidities and not socioeconomic or behavioral factors. We used machine learning ...

3D microscopy and deep learning reveal the heterogeneity of crown-like structure microenvironments in intact adipose tissue.

Crown-like structures (CLSs) are adipose microenvironments of macrophages engulfing adipocytes. Their histological density in visceral adipose tissue (VAT) predicts metabolic disorder progression in obesity and is believed to initiate obesity comorbidities. Here, we use three-dimensional (3D) light sheet microscopy and deep learning to quantify 3D features of VAT CLSs in lean and obese states. Obe...

HSM6AP: a high-precision predictor for the Homo N6-methyladenosine (m^6 A) based on multiple weights and feature stitching.

Recent studies have shown that RNA methylation modification can affect RNA transcription, metabolism, splicing and stability. In addition, RNA methyla...

Association Between Coffee Intake and Incident Heart Failure Risk: A Machine Learning Analysis of the FHS, the ARIC Study, and the CHS.

BACKGROUND: Coronary heart disease, heart failure (HF), and stroke are complex diseases with multiple phenotypes. While many risk factors for these di...

Assessing Dry Weight of Hemodialysis Patients via Sparse Laplacian Regularized RVFL Neural Network with L-Norm.

Dry weight is the normal weight of hemodialysis patients after hemodialysis. If the amount of water in diabetes is too much (during hemodialysis), the...

Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning.

COVID-19 is a newly emerging infectious disease, which is generally susceptible to human beings and has caused huge losses to people's health. Acute r...

Supervised mutational signatures for obesity and other tissue-specific etiological factors in cancer.

Determining the etiologic basis of the mutations that are responsible for cancer is one of the fundamental challenges in modern cancer research. Diffe...

Deep CHORES: Estimating Hallmark Measures of Physical Activity Using Deep Learning.

Wrist accelerometers for assessing hallmark measures of physical activity (PA) are rapidly growing with the advent of smartwatch technology. Given the...

Asthma-prone areas modeling using a machine learning model.

Nowadays, owing to population growth, increasing environmental pollution, and lifestyle changes, the number of asthmatics has significantly increased....

Ranking of a wide multidomain set of predictor variables of children obesity by machine learning variable importance techniques.

The increased prevalence of childhood obesity is expected to translate in the near future into a concomitant soaring of multiple cardio-metabolic dise...

A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence.

COVID-19 has infected 77.4 million people worldwide and has caused 1.7 million fatalities as of December 21, 2020. The primary cause of death due to C...

Body Mass Index Variable Interpolation to Expand the Utility of Real-world Administrative Healthcare Claims Database Analyses.

INTRODUCTION: Administrative claims data provide an important source for real-world evidence (RWE) generation, but incomplete reporting, such as for b...

Screening of sleep apnea based on heart rate variability and long short-term memory.

PURPOSE: Sleep apnea syndrome (SAS) is a prevalent sleep disorder in which apnea and hypopnea occur frequently during sleep and result in increase of ...

An artificial intelligence-derived tool proposal to ease disordered eating screening in people with obesity.

PURPOSE: In people with obesity, food addiction (FA) tends to be associated with poorer outcomes. Its diagnosis can be challenging in primary care. Ba...

Validation of the Khorana Score for Prediction of Venous Thromboembolism After Robot-Assisted Radical Cystectomy.

The Khorana score (KS) is used to predict the risk of venous thromboembolism (VTE) for cancer patients. We sought to assess the association between K...

Renoprotective role of bariatric surgery in patients with established chronic kidney disease.

BACKGROUND: Bariatric surgery (BS) has been postulated as the most effective measure for weight reduction. Weight loss improves metabolic parameters a...

Laparoscopic cryoablation for small renal masses: Oncological outcomes at 5-year follow-up.

: To evaluate the oncological outcome at 5-year follow-up after laparoscopic cryoablation (LCA) for small renal masses (SRMs), as there is an increasi...

Assessing the signal quality of electrocardiograms from varied acquisition sources: A generic machine learning pipeline for model generation.

BACKGROUND AND OBJECTIVE: Long-term electrocardiogram monitoring comes at the expense of signal quality. During unconstrained movements, the electroca...

The Utility of Artificial Neural Networks for the Non-Invasive Prediction of Metabolic Syndrome Based on Personal Characteristics.

This study investigated the diagnostic accuracy of using an artificial neural network (ANN) for the prediction of metabolic syndrome (MetS) based on s...

Machine learning to reveal hidden risk combinations for the trajectory of posttraumatic stress disorder symptoms.

The nature of the recovery process of posttraumatic stress disorder (PTSD) symptoms is multifactorial. The Massive Parallel Limitless-Arity Multiple-t...