Artificial intelligence (AI) is rapidly reshaping cancer research and personalized clinical
care. Availability of high-dimensionality datasets coupled with advances in high …

There is an avalanche of biomedical data generation and a parallel expansion in
computational capabilities to analyze and make sense of these data. Starting with genome …

In the past decade, the emergence of machine learning (ML) applications has led to
significant advances towards implementation of personalised medicine approaches for …

Electronic health records (EHR) contain large volumes of unstructured text, requiring the
application of information extraction (IE) technologies to enable clinical analysis. We present …

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

Background Heterogeneity in type 2 diabetes presentation and progression suggests that
precision medicine interventions could improve clinical outcomes. We undertook a …

Neurologists nowadays no longer view neurodegenerative diseases, like Parkinson's and
Alzheimer's disease, as single entities, but rather as a spectrum of multifaceted symptoms …

Intensive care units (ICU) are increasingly looking towards machine learning for methods to
provide online monitoring of critically ill patients. In machine learning, online monitoring is …

Hepatitis C is an infectious disease that affects more than 70 million people worldwide, even
killing 400 thousand of them annually. To better understand this disease and its prognosis …

Background Alzheimer's disease (AD) is a highly heterogeneous disease with diverse
trajectories and outcomes observed in clinical populations. Understanding this …