Predicting patients' risk of developing certain diseases is an important research topic in
healthcare. Accurately identifying and ranking the similarity among patients based on their …

Pairwise learning has received much attention recently as it is more capable of modeling the
relative relationship between pairs of samples. Many machine learning tasks can be …

Prediction of response to input drivers by unmonitored entities has been recognized as one
of the most important problems in many scientific problems. This problem is challenging due …

A clinically meaningful distance metric, which is learned from measuring patient similarity,
plays an important role in clinical decision support applications. Several metric learning …

As an effective way to learn a distance metric between pairs of samples, deep metric
learning (DML) has drawn significant attention in recent years. The key idea of DML is to …

Patient similarity learning aims to use patient information such as electronic medical records
and genetic data as input to calculate the pairwise similarity between patients, and it is …

Automatic representation learning of key entities in electronic health record (EHR) data is a
critical step for healthcare informatics that turns heterogeneous medical records into …

Metric learning aims to learn a good distance metric that can capture the relationships
among instances, and its importance has long been recognized in many fields. In the …

Meta-learning enables quick adaptation of machine learning models to new tasks with
limited data. While tasks could come from varying distributions in reality, most of the existing …

The goal of metric learning is to learn a good distance metric that can capture the
relationships among instances, and its importance has long been recognized in many fields …