Obesity

Decoding movement direction from cortical microelectrode recordings using an LSTM-based neural network.

Brain-machine interfaces (BMIs) allow individuals to communicate with computers using neural signals, and Kalman Filter (KF) are prevailingly used to decode movement directions from these neural signals. In this paper, we implemented a multi-layer long short-term memory (LSTM)based artificial neural network (ANN) for decoding BMI neural signals. We collected motor cortical neural signals from a no...

Machine Learning-Based Identification of Obesity from Positive and Unlabelled Electronic Health Records.

INTRODUCTION: Prevalence of overweight and obesity are increas- ing in the last decades, and with them, diseases and health conditions such as diabetes, hypertension or cardiovascular diseases. However, hos- pital databases usually do not record such conditions in adults, neither anthropomorfic measures that facilitate their identification.

The Impact of Robotics in Learning Roux-en-Y Gastric Bypass: a Retrospective Analysis of 214 Laparoscopic and Robotic Procedures : Robotic Vs. Laparoscopic RYGB.

BACKGROUND: Proximal Roux-en-Y gastric bypass is commonly used to manage obesity, performed using laparoscopic or robot-assisted minimally invasive su...

Amasyali and Baldwin, Editorial Comment on: The Impact of Obesity in Patients Undergoing Robotic Partial Nephrectomy by Rosen et al. (J Endourol 2020;33(6):431-437; DOI: 10.1089/end.2019.0018).

Identifying Schizo-Obsessive Comorbidity by Tract-Based Spatial Statistics and Probabilistic Tractography.

A phenomenon in schizophrenia patients that deserves attention is the high comorbidity rate with obsessive-compulsive disorder (OCD). Little is known ...

Classification Techniques for Cardio-Vascular Diseases Using Supervised Machine Learning.

INTRODUCTION: The World Health Organization has estimated that 12 million deaths occur worldwide, every year due to Heart diseases. Half the deaths in...

[A Wearable System for Cervical Spondylosis Prevention Based on Artificial Intelligence].

Accompanied by changes in modern work and lifestyle, the incidence of cervical spondylosis has increased year by year. In view of the fact long-term f...

Deep Learning for Acute Myeloid Leukemia Diagnosis.

By changing the lifestyle and increasing the cancer incidence, accurate diagnosis becomes a significant medical action. Today, DNA microarray is widel...

Fuzzy cognitive map based approach for determining the risk of ischemic stroke.

Stroke is the third major cause of mortality in the world. The diagnosis of stroke is a very complex issue considering controllable and uncontrollable...

A computational study of mental health awareness campaigns on social media.

As public discourse continues to progress online, it is important for mental health advocates, public health officials, and other curious parties and ...

Randomized controlled trial of OnTrack, a just-in-time adaptive intervention designed to enhance weight loss.

Individual instances of nonadherence to reduced calorie dietary prescriptions, that is, dietary lapses, represent a key challenge for weight managemen...

Predicting post-experiment fatigue among healthy young adults: Random forest regression analysis.

The current study utilized a random forest regression analysis to predict post-experiment fatigue in a sample of 212 healthy participants (mean age = ...

Sarcopenia feature selection and risk prediction using machine learning: A cross-sectional study.

The purpose of this study was to verify the usefulness of machine learning (ML) for selection of risk factors and development of predictive models for...

Interaction Effects of Gene Polymorphisms and Obesity on Nonalcoholic Fatty Liver Disease.

[Artificial Intelligence for Diagnostic Support of Neuroimage].

Artificial intelligence (AI) shows promises in terms of diagnostic support on neuroimaging. We developed a software that predicts Alzheimer's disease ...

[Use AI for Dementia Diagnosis].

Using AI for dementia diagnosis is still in research stage, however some of the work points to the development of new medical devices. One is a voxel-...

[Brain-Machine Interface and Neuro-Rehabilitation].

Brain-Machine Interface (BMI) is a technology that enables users to control computers/machines intuitively via their volitional brain activities. Cont...

Borderline Personality Features in Inpatients with Bipolar Disorder: Impact on Course and Machine Learning Model Use to Predict Rapid Readmission.

BACKGROUND: Earlier research indicated that nearly 20% of patients diagnosed with either bipolar disorder (BD) or borderline personality disorder (BPD...

Sleep Apnea Severity Estimation from Respiratory Related Movements Using Deep Learning.

Sleep apnea is a common chronic respiratory disorder which occurs due to the repetitive complete or partial cessations of breathing during sleep. The ...

Building robust models for Human Activity Recognition from raw accelerometers data using Gated Recurrent Units and Long Short Term Memory Neural Networks.

Human Activity Recognition (HAR) is a growing field of research in biomedical engineering and it has many potential applications in the treatment and ...