Auscultation plays an important role in the clinic, and the research community has been
exploring machine learning (ML) to enable remote and automatic auscultation for respiratory …

To identify Coronavirus disease (COVID-19) cases efficiently, affordably, and at scale, recent
work has shown how audio (including cough, breathing and voice) based approaches can …

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 …

Audio signals are widely recognised as powerful indicators of overall health status, and
there has been increasing interest in leveraging sound for affordable COVID-19 screening …

High model performance, on average, can hide that models may systematically
underperform on subgroups of the data. We consider the tabular setting, which surfaces the …

Symbolic learning is the logic-based approach to machine learning, and its mission is to
provide algorithms and methodologies to extract logical information from data and express it …

Deep learning is increasingly used for decision-making in health applications. However,
commonly used deep learning models are deterministic and are unable to provide any …

The Covid-19 pandemic is one of the most significant global health concerns that have
emerged in this decade. Intelligent healthcare technology and techniques based on speech …

COVID-19 and heart failure (HF) are common disorders and although they share some
similar symptoms, they require different treatments. Accurate diagnosis of these disorders is …

Uncertainty quantification is critical for ensuring the safety of deep learning-enabled health
diagnostics, as it helps the model account for unknown factors and reduces the risk of …