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CHIL 2021 : ACM Conference on Health, Inference, and Learning
CHIL 2021 : ACM Conference on Health, Inference, and Learning

CHIL 2021 : ACM Conference on Health, Inference, and Learning

Virtual
Event Date: April 08, 2021 - April 10, 2021
Abstract Submission Deadline: January 07, 2021
Submission Deadline: January 11, 2021
Notification of Acceptance: February 15, 2021
Camera Ready Version Due: March 05, 2021



About

The ACM Conference on Health, Inference, and Learning (ACM-CHIL), targets a cross-disciplinary representation of clinicians and researchers (from industry and academia) in machine learning, health policy, causality, fairness, and other related areas.

ACM-CHIL 2021 will be a virtual conference building on the success of the inaugural ACM-CHIL conference held digitally in July 2020, as well as the ML4H Unconference, held in Toronto, and the Machine Learning for Health Workshop at NeurIPS, held in Vancouver. The conference is designed to spark insight-driven discussions on new and emerging ideas that may lead to collaboration and discussion.


Call for Papers

Works submitted to ACM-CHIL will be reviewed by 3 reviewers within the broader field of machine learning for healthcare. Reviewers will be asked to primarily judge the work according to five criteria:

Relevance: All submissions to ACM-CHIL are expected to be relevant to health. Concretely, this means that the problem is well-placed into the relevant themes for the conference. We will instruct reviewers to gauge whether submissions are best suited for this track, or should be moved elsewhere.

Quality: Is the submission technically sound? Are claims well supported by theoretical analysis or experimental results? Is this a complete piece of work or work in progress? Are the authors careful and honest about evaluating both the strengths and weaknesses of their work?

Originality: Are the tasks or methods new? Is the work a novel combination of well-known techniques? Is it clear how this work differs from previous contributions? Is related work adequately cited?

Clarity: Is the submission clearly written? Is it well organized? (If not, please make constructive suggestions for improving its clarity.) Does it adequately inform the reader? (Note: a superbly written paper provides enough information for an expert reader to reproduce its results.)

Significance: Are the results important? Are others (researchers or practitioners) likely to use the ideas or build on them? Does the submission address a difficult task in a better way than previous work? Does it advance the state of the art in a demonstrable way? Does it provide unique data, unique conclusions about existing data, or a unique theoretical or experimental approach?

Final decisions will be made in accordance with the reviewer’s overall judgement, along with their subjective ratings of confidence/expertise, and according to our own editorial judgement.



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Summary

CHIL 2021 : ACM Conference on Health, Inference, and Learning will take place in Virtual. It’s a 3 days event starting on Apr 08, 2021 (Thursday) and will be winded up on Apr 10, 2021 (Saturday).

CHIL 2021 falls under the following areas: HEALTHCARE, HEALTH INFORMATICS, MACHINE LEARNING, etc. Submissions for this Conference can be made by Jan 11, 2021. Authors can expect the result of submission by Feb 15, 2021. Upon acceptance, authors should submit the final version of the manuscript on or before Mar 05, 2021 to the official website of the Conference.

Please check the official event website for possible changes before you make any travelling arrangements. Generally, events are strict with their deadlines. It is advisable to check the official website for all the deadlines.

Other Details of the CHIL 2021

  • Short Name: CHIL 2021
  • Full Name: ACM Conference on Health, Inference, and Learning
  • Timing: 09:00 AM-06:00 PM (expected)
  • Fees: Check the official website of CHIL 2021
  • Event Type: Conference
  • Website Link: https://www.chilconference.org/
  • Location/Address: Virtual


Credits and Sources

[1] CHIL 2021 : ACM Conference on Health, Inference, and Learning


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