The field of machine learning is witnessing its golden era as deep learning slowly becomes
the leader in this domain. Deep learning uses multiple layers to represent the abstractions of …

Deep learning describes a class of machine learning algorithms that are capable of
combining raw inputs into layers of intermediate features. These algorithms have recently …

Health care is one of the most exciting frontiers in data mining and machine learning.
Successful adoption of electronic health records (EHRs) created an explosion in digital …

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

Survival analysis is a subfield of statistics where the goal is to analyze and model data
where the outcome is the time until an event of interest occurs. One of the main challenges …

Modern electronic health records (EHRs) provide data to answer clinically meaningful
questions. The growing data in EHRs makes healthcare ripe for the use of machine learning …

Currently available risk prediction methods are limited in their ability to deal with complex,
heterogeneous, and longitudinal data such as that available in primary care records, or in …

The continuously increasing cost of the US healthcare system has received significant
attention. Central to the ideas aimed at curbing this trend is the use of technology in the form …

The influx of deep learning (DL) techniques into the field of survival analysis in recent years
has led to substantial methodological progress; for instance, learning from unstructured or …

We describe a new approach to estimating relative risks in time-to-event prediction problems
with censored data in a fully parametric manner. Our approach does not require making …