The increasing availability of biomedical data from large biobanks, electronic health records,
medical imaging, wearable and ambient biosensors, and the lower cost of genome and …

Objectives Patient representation learning refers to learning a dense mathematical
representation of a patient that encodes meaningful information from Electronic Health …

Objective Temporal electronic health records (EHRs) contain a wealth of information for
secondary uses, such as clinical events prediction and chronic disease management …

Traditional machine learning methods face unique challenges when applied to healthcare
predictive analytics. The high-dimensional nature of healthcare data necessitates labor …

With the increasing availability of Electronic Health Records (EHRs) and advances in deep
learning techniques, developing deep predictive models that use EHR data to solve …

The prevalence of multimorbidity has been increasing in recent years, posing a major
burden for health care delivery and service. Understanding its determinants and impact is …

The goal of mortality prediction task is to predict the future death risk of patients according to
their previous Electronic Healthcare Records (EHR). The main challenge of mortality …

Machine learning algorithms have had a profound impact on the field of computer science
over the past few decades. The performance of these algorithms heavily depends on the …

It is of great interest to develop and introduce new techniques to automatically and efficiently
analyze the enormous amount of data generated in today's hospitals, using state-of-the-art …

A digital-twin based three-tiered system is proposed to prioritise patients for urgent intensive
care and ventilator support. The deep learning methods are used to build patient-specific …