Friday, May 5, 2023 - Virtual

Workshop on
Time Series Representation Learning for Health

ICLR 2023

Motivation

Time series data have been used in many applications in healthcare, such as the diagnosis of a disease, prediction of disease progression, clustering of patient groups, online monitoring, and dynamic treatment regimes, to name a few. More and more methods build on representation learning to tackle these problems by first learning a (typically low-dimensional) representation of the time series and then use the learned representation for the corresponding downstream task.

Machine learning (ML) provides a powerful set of tools for time series data; however, its applicability in healthcare is still limited. As a result, the potential of time series analysis has yet to be fully realized. Our workshop on 'Time Series Representation Learning for Health' aims at bringing together the community to discuss cutting-edge research in this area, with a focus on the following themes:

Labeling, in general and in particular of long-term recordings, is a nontrivial task requiring appropriate experts like clinicians who are restricted in their time
Time series data acquired within real-life settings and novel measurement modalities are recorded without supervision, having no labels at all
The high dimensionality of data from multimodal sources
Missing values or outliers within acquired data or irregularity of measured data

This workshop focuses on these aspects and the potential benefits of integrating representation learning in time series applications. Our goal is to encourage a discussion around developing new ideas towards representation learning complemented with robust, interpretable, and explainable approaches which can provide a medical expert with more information than just a prediction result.

To make time series representation learning research actionable in clinical practice, we especially encourage discussions from application areas that tackle minority data groups and, thus, have their own unique challenges; for example, pediatrics, critical care (ICU), rare diseases like Alzheimer, HIV, fertility, and others.

The workshop will include talks from leading researchers and pioneers in ML as well as spotlight presentations and poster sessions for accepted papers (see full schedule).