Fwd: [ML-news] CfP for NeurIPS 2021 Workshop Bridging the Gap: from Machine Learning Research to Clinical Practice

[Artur Dubrawski]

This is potentially relevant to many of us. I think we could potentially
submit a handful of papers.

This looks like an obvious response of the community to the - unwise in my
personal opinion - decision of organizers of the ML4H - a very successful
series of NeurIPS workshops started a while back by our own Ina Fiterau -
to decapsulate it from NeurIPS program and set it up as a standalone
symposium.

Artur

---------- Forwarded message ---------
From: 'vogt.... at googlemail.com' via Machine Learning News <
ml-news at googlegroups.com>
Date: Tue, Aug 10, 2021 at 11:25 AM
Subject: [ML-news] CfP for NeurIPS 2021 Workshop Bridging the Gap: from
Machine Learning Research to Clinical Practice
To: Machine Learning News <ml-news at googlegroups.com>


We are pleased to announce the NeurIPS 2021 Workshop *Bridging the Gap:
from Machine Learning Research to Clinical Practice*

 Call for Papers:
https://sites.google.com/g.harvard.edu/research2clinics/call-for-papers

* About the workshop:*

In this workshop, we aim to bring together ML researchers and clinicians to
discuss the challenges and potential solutions on how to enable the use of
state-of-the-art ML techniques in the daily clinical practice and
ultimately improve healthcare.

We invite submissions describing innovative research focused on bridging
the gap between ML research and application in clinical practice. Authors
are invited to submit works that fit anywhere within the broad overview of
our workshop. To incentivize high-quality submissions of novel work, which
should be compelling and highly relevant to research and clinics, we will
be awarding Best Paper Awards

 We are specifically interested (but are not limited to) the following
areas:


   - Procedures that bring humans-in-the-loop for auditing ML healthcare
   systems to improve human performance, machine performance, or both.
   - Methods that are robust to changes in population, distribution shifts,
   or other types of biases.
   -  Properties of ML methods/systems to be fulfilled to successfully
   deploy them in the clinic where the feasibility of these properties should
   also be taken into account.
   - Analyses of how to assess the failure modes of ML models for
   healthcare and reduce over-reliance.
   - Developing methods for improved interpretability of ML predictions in
   the context of healthcare.
   - Translational and implementational aspects: challenges and lessons
   learned from integrating an ML system into clinical workflow.

More information can be found on our website:
https://sites.google.com/g.harvard.edu/research2clinics/home

*Important dates:*

* Submissions Deadline: Sept 17, 2021 11:59 AoE

* Authors Notification: Oct 16, 2021 11:59 AoE



*Organizers:*

Julia Vogt (ETH Zurich)

Ece Özkan (ETH Zurich)

Sonali Parbhoo (Harvard University)

Jiayu Yao (Harvard University)

Shengpu Tang (University of Michigan)

Melanie F. Pradier (MSR)

Patrick Schwab (GSK)

Mario Wieser (Genedata AG)





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