Background Medical practitioners use survival models to explore and understand the
relationships between patients' covariates (eg clinical and genetic features) and the …

Current medical practice is driven by clinical guidelines which are designed for the
“average” patient. Deep learning is enabling medicine to become personalized to the patient …

Survival analyses of populations and the establishment of prognoses for individual patients
are important activities in the practice of medicine. Standard survival models, such as the …

Survival analysis has been developed and applied in the number of areas including
manufacturing, finance, economics and healthcare. In healthcare domain, usually clinical …

In this work, we propose a new Transformer-based survival model which estimates the
patient-specific survival distribution. Our contributions are twofold. First, to the best of our …

There is currently great interest in applying neural networks to prediction tasks in medicine. It
is important for predictive models to be able to use survival data, where each patient has a …

Introduction Survival period prediction through early diagnosis of cancer has many benefits.
It allows both patients and caregivers to plan resources, time and intensity of care to provide …

Background Access to palliative care is a key quality metric which most healthcare
organizations strive to improve. The primary challenges to increasing palliative care access …

There has been increasing interest in modelling survival data using deep learning methods
in medical research. Current approaches have focused on designing special cost functions …

Translating the vast data generated by genomic platforms into accurate predictions of
clinical outcomes is a fundamental challenge in genomic medicine. Many prediction …