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ACM Conference on Health, Inference, and Learning

CHIL 2021

URL: https://www.chilconference.org/
Event Date: 2021-04-08 ~ 2021-04-10
Abstract Submission Date: 2021-01-07
Submission Date: 2021-01-11

The ACM Conference on Health, Inference, and Learning (CHIL) solicits work across a variety of disciplines, including machine learning, statistics, epidemiology, health policy, operations, and economics. ACM-CHIL 2021 invites submissions touching on topics focused on relevant problems affecting health. Specifically, authors are invited to submit 8-10 page papers (with unlimited pages for references) to each of the tracks described below.
To ensure that all submissions to ACM-CHIL are reviewed by a knowledgeable and appropriate set of reviewers, the conference is divided into tracks and areas of interest. Authors will select exactly one primary track and area of interest when they register their submissions, in addition to one or more sub-disciplines.
Track Chairs will oversee the reviewing process. In case you are not sure which track your submission fits under, feel free to contact the Track or Proceedings Chairs for clarification. The Proceedings Chairs reserve the right to move submissions between tracks and/or areas of interest if the Proceedings Chairs believe that a submission has been misclassified.
Important Dates
Abstracts due – January 7, 2021
Submissions due – January 11, 2021 (11:59 pm AoE)
Notification of Acceptance – Feb 12, 2021 (11:59 pm AoE)
Camera Ready Due – March 5, 2021 (11:59 pm AoE)
Conference Date – April 8-10, 2021
Tracks
Track 1: Models and Methods
Track 2: Applications and Practice
Track 3: Policy: Impact and Society
Sub-Disciplines
These are called topics in the submission form. Authors should select one or more discipline(s) in machine learning for health (ML4H) from the following list when submitting their paper: benchmark datasets, distribution shift, transfer learning, population health, social networks, scalable ML4H systems, natural language processing (NLP), computer vision, time series, bias/fairness, causality, *-omics, wearable-data, etc. Peer reviewers are assigned according to expertise in the sub-discipline(s) selected, so please choose your relevant topics carefully.

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ACM Conference on Health, Inference, and Learning

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