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

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.