Breast cancer is a kind of cancer that forms in the cells of the breasts. After skin cancer, breast cancer represents the most common cancer diagnosed in women in the United States. As a matter of fact, in January 2022, there are more than 3.8 million ...

Deploying bio-electrical signals and image processing (visual) techniques are the two popular means to provide input to generate grasp control for robotic and prosthetic devices. Visual perception-based techniques rely on computationally expensive image ...

We propose cross-modal attentive connections, a new dynamic and effective technique for multimodal representation learning from wearable data. Our solution can be integrated into any stage of the pipeline, i.e., after any convolutional layer or block, to ...

Cohort study is one of the most commonly used study methods in medical and public health researches, which result in longitudinal data. Conventional statistical models and machine learning methods are not capable of modeling the evolution trend of the ...

The quantification of emotional states is an important step to understanding wellbeing. Time series data from multiple modalities such as physiological and motion sensor data have proven to be integral for measuring and quantifying emotions. Monitoring ...

SummerTime seeks to summarize global time-series signals and provides a fixed-length, robust representation of the variable-length time series. Many machine learning methods depend on data instances with a fixed number of features. As a result, those ...

Sleep apnea is a common yet severely under-diagnosed sleep related disorder. Unattended sleep monitoring at home with low-cost sensors can be leveraged for condition detection, and Machine Learning offers a generalized solution for this task. However, ...

Childhood obesity is a major public health challenge. Early prediction and identification of the children at an elevated risk of developing childhood obesity may help in engaging earlier and more effective interventions to prevent and manage obesity. Most ...

Machine Learning (ML) is increasingly used to support decision-making in the healthcare sector. While ML approaches provide promising results with regard to their classification performance, most share a central limitation, their black-box character. This ...

Early detection and treatment of glaucoma is of interest as it is a chronic eye disease leading to an irreversible loss of vision. Existing automated systems rely largely on fundus images for assessment of glaucoma due to their fast acquisition and cost-...

Social media analytics can considerably contribute to understanding health conditions beyond clinical practice, by capturing patients’ discussions and feelings about their quality of life in relation to disease treatments. In this article, we propose a ...

The electrocardiogram (ECG) has been proven as an efficient diagnostic tool to monitor the electrical activity of the heart and has become a widely used clinical approach to diagnose heart diseases. In a practical way, the ECG signal can be decomposed ...

Sleep apnea is a common and strongly under-diagnosed severe sleep-related respiratory disorder with periods of disrupted or reduced breathing during sleep. To diagnose sleep apnea, sleep data are collected with either polysomnography or polygraphy and ...

Preventable adverse events as a result of medical errors present a growing concern in the healthcare system. As drug-drug interactions (DDIs) may lead to preventable adverse events, being able to extract DDIs from drug labels into a machine-processable ...

This article presents an in-depth experimental study of Ensemble Deep Learning techniques on small datasets for the analysis of time-series data generated by wearable devices. Deep Learning networks generally require large datasets for training. In some ...