NIPS Workshop on Machine Learning for Health, Long Beach, Vereinigte Staaten

Abbrevation

NIPS ML4H

Link

https://ml4health.github.io/2017/

City

Long Beach

Country

United States

Deadline Paper

2017-10-30

Start Date

2017-12-08

End Date

2017-12-08

Abstract

The goal of the Machine Learning for Health Workshop (NIPS ML4H 2017) is to foster collaborations that meaningfully impact medicine by bringing together clinicians, health data experts, and machine learning researchers. We aim to build on the success of the last two NIPS ML4H workshops which were widely attended and helped form the foundations of a new research community. This year’s program emphasizes identifying previously unidentified problems in healthcare that the machine learning community hasn′t addressed, or seeing old challenges through a new lens. While healthcare and medicine are often touted as prime examples for disruption by AI and machine learning, there has been vanishingly little evidence of this disruption to date. To interested parties who are outside of the medical establishment (e.g. machine learning researchers), the healthcare system can appear byzantine and impenetrable, which results in a high barrier to entry. In this workshop, we hope to reduce this activation energy by bringing together leaders at the forefront of both machine learning and healthcare for a dialog on areas of medicine that have immediate opportunities for machine learning. Attendees at this workshop will quickly gain an understanding of the key problems that are unique to healthcare and how machine learning can be applied to addressed these challenges. The workshop will feature invited talks from leading voices in both medicine and machine learning. Invited clinicians will discuss open clinical problems where data–driven solutions can make an immediate difference. The workshop will conclude with an interactive panel discussion where all speakers respond to questions provided by the audience. From the research community, we welcome short paper submissions highlighting novel research contributions at the intersection of machine learning and healthcare. Accepted submissions will be featured as poster presentations and (in select cases) as short oral spotlight presentations. Submitted papers should describe innovative machine learning research focused on relevant problems in health and medicine. This can mean new models, new datasets, new algorithms, or new applications. Topics of interest include but are not limited to reinforcement learning, temporal models, deep learning, semi–supervised learning, data integration, learning from missing or biased data, learning from non–stationary data, model criticism, model interpretability, causality, model biases, and transfer learning.