The integration of machine/deep learning and sensing technologies is transforming
healthcare and medical practice. However, inherent limitations in healthcare data, namely …

The integration of machine learning (ML) in healthcare has witnessed remarkable
advancements, transforming the landscape of medical diagnosis, treatment, and overall …

Machine learning is increasingly integrated into clinical practice, with applications ranging
from pre-clinical data processing, bedside diagnosis assistance, patient stratification …

A main goal of Precision Medicine is that of incorporating and integrating the vast corpora on
different databases about the molecular and environmental origins of disease, into analytic …

Gaining knowledge and actionable insights from complex, high-dimensional and
heterogeneous biomedical data remains a key challenge in transforming health care …

Data generated from sources such as wearable sensors, medical imaging, personal health
records, and public health organizations have resulted in a massive information increase in …

Advances in wearable sensing and mobile computing have enabled the collection of health
and well-being data outside of traditional laboratory and hospital settings, paving the way for …

Recent advances in noninvasive portable and wearable biosensors have attracted
significant attention due to their capability to offer continual physiological information for …

The sixth Machine Learning for Health (ML4H) workshop 1 was held virtually on December
11, 2020, in conjunction with the Thirty-fourth Conference on Neural Information Processing …

Medicine is undergoing an unprecedented digital transformation, as massive amounts of
health data are being produced, gathered, and curated, ranging from in-hospital (eg …