ML4H 2020 invites submissions describing innovative machine learning research focused on relevant problems in health and biomedicine. Similar to last year, ML4H 2020 will both accept papers for a formal proceedings and accept traditional, non-archival extended abstract submissions. Authors are invited to submit works for either track provided the work fits within the purview of Machine Learning for Health. In addition, we especially solicit works that speak to this year’s ML4H theme: Advancing Healthcare for All.

We are also piloting mentorship programs for authors and reviewers. See https://ml4health.github.io/pages/mentorship.html for additional details.

===== Important Dates =====
- Monday, Sep. 28: Submission Title/Summary Paragraph Deadline
- Friday, Oct. 2: Submission Deadline
- Friday, Oct. 16: Reviews Due
- Tuesday, Oct. 20: Limited Author Response Deadline
- Wednesday, Oct. 28th: Final Decisions Released
- Friday or Saturday, Dec 11-12, 2020: Virtual Workshop

===== Theme & Topics =====
We invite submissions from all areas of machine learning for health and biomedicine but we especially encourage submissions that focus on this year’s theme, Advancing Healthcare for All.

The application of machine learning to healthcare is often characterised by the development of cutting-edge technology aiming to improve patient outcomes. By developing sophisticated models on high-quality datasets we hope to better diagnose, forecast, and otherwise characterise the health of individuals. At the same time, when we build tools which aim to assist highly-specialised caregivers, we limit the benefit of machine learning to only those who can access such care. The fragility of healthcare access both globally and locally prompts us to ask, “How can machine learning be used to help enable healthcare for all?”

To this end, we will actively encourage the submission and presentation of new work which explores any of the following topics:
- Accessible diagnostic and prognostic systems
- Health equity
- Fairness and bias in machine learning systems
- Generalisation across populations or systems
- Improving patient participation in health
- Augmenting and supporting the capabilities of healthcare workers
- Rare or underserved diseases
- Democratising ML4H research
- Non-traditional delivery of healthcare

===== Awards =====
For the first time, thanks to sponsorship from Roche, ML4H will grant awards to exceptional papers and reviewers. In particular, at this time we plan to feature the following awards:
- Best Newcomer Submission: This award will honor the best submission from either track with a first author who has not had prior work accepted to the workshop before.
- Best Thematic Submission: This award will honor the best submission from either track that is aligned with this year’s theme: Advancing Healthcare for All.
- Top Reviewer Award: This award will honor the top reviewers.

Awardees will be recognized at the event directly, and more prizes may be announced at a later date. Check back in later to learn more details!

===== Mentorship Opportunities =====
This year, ML4H will also be piloting mentorship programs for authors and reviewers. We especially encourage less experienced authors and reviewers and participants from underrepresented backgrounds to sign up as mentees, as well as more senior community members to serve as mentors for these programs.

For more details, see here: https://ml4health.github.io/2020/pages/mentorship.html

===== Submission Tracks =====
Like last year, ML4H 2020 will feature two submission tracks: a full, archival proceedings track and a non-archival, extended abstract track. Submissions to either track will undergo double-blind peer review. It will be up to the authors to ensure the proper anonymization of their papers. Do not include any names or affiliations. Refer to your own past work in the third-person. Malformed, non-blinded, non-healthcare oriented, or grossly insufficient works may be desk rejected without undergoing additional review. In addition, submissions to both tracks will be featured at the workshop’s virtual poster session, and a subset of works (from either track) will be invited to give a spotlight presentation about their work.

Accepted papers and extended abstracts will both be chosen based on technical merit and suitability to the workshop's goals. More details on how to write an excellent ML4H paper or extended abstract can be found here: https://ml4health.github.io/2020/pages/writing-guidelines.html

We strongly encourage examining this document, as they will also be used to communicate to reviewers what features of submissions are most important to this venue. Below are the salient differences between both tracks.

=== Proceedings Track ===
Full proceedings papers can be up to 8 pages (excluding references). Excellent ML4H Proceedings papers should be compelling, cohesive works with a high degree of technical sophistication as well as clear and high-impact relevance to healthcare.
Papers that are submitted to the ML4H proceedings track should follow the general NeurIPS dual submission policies, and most notably cannot be already published or under review in any other archival venue. Similarly, papers published to the ML4H proceedings cannot be published again later at any other venue.

=== Extended Abstract Track ===
Non-archival extended abstracts can be up to 4 pages (excluding references), though additional information not critical for understanding the work can be included in an appendix without penalty (reviewers will review the work based predominantly on the main text).
An excellent extended abstract is one that leads to insight at the workshop through interaction with other attendees. This can be through presenting new ideas/ways of thinking, leading to insightful discussion and feedback, dissemination new valuable resources, or enabling new opportunities for collaborations. We also especially solicit “non-traditional research artifacts” as submissions to the extended abstract track, such as papers highlighting novel datasets, insightful negative results, exciting preliminary results that warrant rapid dissemination, reproducibility studies, and opinion pieces or critiques. Authors of accepted extended abstracts (non-archival submissions) retain full copyright of their work, and acceptance of such a submission to NeurIPS ML4H 2020 does not preclude publication of the same material in another journal or conference. Furthermore, extended abstract submissions that are under review or have been recently published in a conference or a journal are allowed for submission. Authors should clearly state any overlapping published or submitted work at the time of submission, and must ensure that they are not violating any other venue dual submission policies.

===== Submission Instructions =====
Researchers interested in contributing will need to first submit their submission title and abstract (simply a short summary paragraph for extended abstract submissions, not the full submission) by Monday, Sep. 28, 2020, 11:59 PM anywhere on earth (AoE). Submission metadata may undergo minor modifications during final submission, but submitting a title/abstract is required for a full submission to be considered. Full submissions will be due on Fri, Oct 2, 2020, 11:59 PM AoE, and will be in the form of anonymized PDF files. At the time of submission, authors will indicate whether they would like the submission to be in the proceedings track (up to 8 pages excluding references) or the extended abstract track (up to 4 pages excluding references).

A submission link and additional details regarding the two submission tracks will be provided on our website at least two weeks prior to the deadline: https://ml4health.github.io

We will have a limited author response period after reviews are released, but we stress that this response period is not a formal rebuttal to reviewer claims, and is instead merely a vehicle to flag severe issues or critical misunderstandings for the meta-reviewer to consider (for example, if the review appears to be for the wrong paper or suffers from a major and transparently verifiable misunderstanding about the work). If the meta-reviewer cannot review your provided response in under 20 minutes and determine that the reviewer made a critical error that clearly affected their recommendation of the paper, the response will be disregarded and will not play a role in the final decision. No new experiments, results/analyses, explanations, text, or additional references (or promises to add such) will be considered in this response period, and submitting a response is in no way required.

To promote community interaction, at least one presenting author of accepted works must register for the workshop.
Registration information can be found here: https://nips.cc/Register/view-registration.

Please direct questions to: ml4h.workshop.neurips.2020@gmail.com